diff --git a/lib/node_modules/@stdlib/_tools/github/user-repos/lib/defaults.json b/lib/node_modules/@stdlib/_tools/github/user-repos/lib/defaults.json
index 660b83ba62b4..c49cc78af642 100644
--- a/lib/node_modules/@stdlib/_tools/github/user-repos/lib/defaults.json
+++ b/lib/node_modules/@stdlib/_tools/github/user-repos/lib/defaults.json
@@ -1,13 +1,13 @@
{
- "protocol": "https",
- "hostname": "api.github.com",
- "port": 443,
- "pathname": "/",
- "page": 1,
- "last_page": "last",
- "per_page": 100,
- "query": "",
- "method": "GET",
- "useragent": "https://github.com/stdlib-js/stdlib/@stdlib/_tools/github/user-repos",
- "accept": "application/vnd.github.v3+json"
+ "protocol": "https",
+ "hostname": "api.github.com",
+ "port": 443,
+ "pathname": "/",
+ "page": 1,
+ "last_page": "last",
+ "per_page": 100,
+ "query": "",
+ "method": "GET",
+ "useragent": "https://github.com/stdlib-js/stdlib/@stdlib/_tools/github/user-repos",
+ "accept": "application/vnd.github.v3+json"
}
diff --git a/lib/node_modules/@stdlib/_tools/lint/filenames/lib/ignore_patterns.json b/lib/node_modules/@stdlib/_tools/lint/filenames/lib/ignore_patterns.json
index 2ff2003075c6..96bf0984420b 100644
--- a/lib/node_modules/@stdlib/_tools/lint/filenames/lib/ignore_patterns.json
+++ b/lib/node_modules/@stdlib/_tools/lint/filenames/lib/ignore_patterns.json
@@ -1,101 +1,101 @@
[
- "**/lint/filenames/test/fixtures/**",
- "node_modules/**",
- "build/**",
- "**/build/**",
- "**/tmp/**",
- "reports/**",
- ".git*",
- "**/*.com",
- "**/*.class",
- "**/*.dll",
- "**/*.exe",
- "**/*.o",
- "**/*.so",
- "**/*.slo",
- "**/*.lo",
- "**/*.obj",
- "**/*.dylib",
- "**/*.lai",
- "**/*.la",
- "**/*.a",
- "**/*.lib",
- "**/*.ko",
- "**/*.elf",
- "**/*.node",
- "**/*.gch",
- "**/*.pch",
- "**/*.out",
- "**/*.app",
- "**/*.7z",
- "**/*.dmg",
- "**/*.gz",
- "**/*.iso",
- "**/*.jar",
- "**/*.rar",
- "**/*.tar",
- "**/*.zip",
- "**/*.log",
- "**/*.sql",
- "**/*.sqlite",
- "**/.DS_Store*",
- "**/._*",
- "**/.Splotlight-V100",
- "**/.Trashes",
- "**/ehthumbs.db",
- "**/Thumbs.db",
- "**/Desktop.ini",
- "**/*~",
- "**/pids",
- "**/*.pid",
- "**/*.seed",
- "**/*.asv",
- "**/*.mex*",
- "**/*.mod",
- "**/*.Rhistory",
- "**/*.Rapp.history",
- "**/*.Rproj.user/",
- "__pycache__/**",
- "**/*.py[cod]",
- "**/*$py.class",
- "**/*.aux",
- "**/*.lof",
- "**/*.log",
- "**/*.lot",
- "**/*.fls",
- "**/*.out",
- "**/*.toc",
- "**/*.dvi",
- "**/*-converted-to.*",
- "**/*.bbl",
- "**/*.bcf",
- "**/*.blg",
- "**/*-blx.aux",
- "**/*-blx.bib",
- "**/*.brf",
- "**/*.run.xml",
- "**/*.fdb_latexmk",
- "**/*.synctex",
- "**/*.synctex.gz",
- "**/*.syntex.gz(busy)",
- "**/*.pdfsync",
- "**/*.alg",
- "**/*.loa",
- "**/acs-*.bib",
- "**/*.thm",
- "**/*.nav",
- "**/*.snm",
- "**/*.vrb",
- "**/*.acn",
- "**/*.acr",
- "**/*.glg",
- "**/*.glo",
- "**/*.gls",
- "**/*-concordance.tex",
- "**/*.tikz",
- "**/*-tikzDictionary",
- "**/*.idx",
- "**/*.ilg",
- "**/*.ind",
- "**/*.ist"
+ "**/lint/filenames/test/fixtures/**",
+ "node_modules/**",
+ "build/**",
+ "**/build/**",
+ "**/tmp/**",
+ "reports/**",
+ ".git*",
+ "**/*.com",
+ "**/*.class",
+ "**/*.dll",
+ "**/*.exe",
+ "**/*.o",
+ "**/*.so",
+ "**/*.slo",
+ "**/*.lo",
+ "**/*.obj",
+ "**/*.dylib",
+ "**/*.lai",
+ "**/*.la",
+ "**/*.a",
+ "**/*.lib",
+ "**/*.ko",
+ "**/*.elf",
+ "**/*.node",
+ "**/*.gch",
+ "**/*.pch",
+ "**/*.out",
+ "**/*.app",
+ "**/*.7z",
+ "**/*.dmg",
+ "**/*.gz",
+ "**/*.iso",
+ "**/*.jar",
+ "**/*.rar",
+ "**/*.tar",
+ "**/*.zip",
+ "**/*.log",
+ "**/*.sql",
+ "**/*.sqlite",
+ "**/.DS_Store*",
+ "**/._*",
+ "**/.Splotlight-V100",
+ "**/.Trashes",
+ "**/ehthumbs.db",
+ "**/Thumbs.db",
+ "**/Desktop.ini",
+ "**/*~",
+ "**/pids",
+ "**/*.pid",
+ "**/*.seed",
+ "**/*.asv",
+ "**/*.mex*",
+ "**/*.mod",
+ "**/*.Rhistory",
+ "**/*.Rapp.history",
+ "**/*.Rproj.user/",
+ "__pycache__/**",
+ "**/*.py[cod]",
+ "**/*$py.class",
+ "**/*.aux",
+ "**/*.lof",
+ "**/*.log",
+ "**/*.lot",
+ "**/*.fls",
+ "**/*.out",
+ "**/*.toc",
+ "**/*.dvi",
+ "**/*-converted-to.*",
+ "**/*.bbl",
+ "**/*.bcf",
+ "**/*.blg",
+ "**/*-blx.aux",
+ "**/*-blx.bib",
+ "**/*.brf",
+ "**/*.run.xml",
+ "**/*.fdb_latexmk",
+ "**/*.synctex",
+ "**/*.synctex.gz",
+ "**/*.syntex.gz(busy)",
+ "**/*.pdfsync",
+ "**/*.alg",
+ "**/*.loa",
+ "**/acs-*.bib",
+ "**/*.thm",
+ "**/*.nav",
+ "**/*.snm",
+ "**/*.vrb",
+ "**/*.acn",
+ "**/*.acr",
+ "**/*.glg",
+ "**/*.glo",
+ "**/*.gls",
+ "**/*-concordance.tex",
+ "**/*.tikz",
+ "**/*-tikzDictionary",
+ "**/*.idx",
+ "**/*.ilg",
+ "**/*.ind",
+ "**/*.ist"
]
diff --git a/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/README.md b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/README.md
new file mode 100644
index 000000000000..01df30975ea1
--- /dev/null
+++ b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/README.md
@@ -0,0 +1,124 @@
+
+
+# hasAlmostEqualValues
+
+> Test if two arrays have respective elements which are approximately equal within a specified number of ULPs (units in the last place).
+
+
+
+
+
+
+
+
+
+
+
+## Usage
+
+```javascript
+var hasAlmostEqualValues = require( '@stdlib/array/base/assert/has-almost-equal-values' );
+```
+
+#### hasAlmostEqualValues( x, y, maxULP )
+
+Tests if two arrays have respective elements which are approximately equal within a specified number of ULPs (units in the last place).
+
+```javascript
+var x = [ 0, 0, 1, 0 ];
+var y = [ 0, 0, 1, 0 ];
+
+var bool = hasAlmostEqualValues( x, y, 1 );
+// returns true
+```
+
+
+
+
+
+
+
+
+
+## Notes
+
+- If provided arrays of unequal length, the function returns `false`.
+- The function does **not** skip `undefined` elements and is thus not optimized for sparse arrays.
+
+
+
+
+
+
+
+
+
+## Examples
+
+
+
+```javascript
+var uniform = require( '@stdlib/random/array/uniform' );
+var Complex128Array = require( '@stdlib/array/complex128' );
+var hasAlmostEqualValues = require( '@stdlib/array/base/assert/has-almost-equal-values' );
+
+var buf = uniform( 10, 0, 10 );
+// returns
+
+var x = new Complex128Array( buf );
+// returns
+
+var y = new Complex128Array( buf );
+// returns
+
+var out = hasAlmostEqualValues( x, y, 1 );
+// returns true
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/benchmark/benchmark.length.js b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/benchmark/benchmark.length.js
new file mode 100644
index 000000000000..9f8c537ff983
--- /dev/null
+++ b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/benchmark/benchmark.length.js
@@ -0,0 +1,96 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var bench = require( '@stdlib/bench' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var isBoolean = require( '@stdlib/assert/is-boolean' ).isPrimitive;
+var zeros = require( '@stdlib/array/base/zeros' );
+var pkg = require( './../package.json' ).name;
+var hasAlmostEqualValues = require( './../lib' );
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} len - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( len ) {
+ var x = zeros( len );
+ return benchmark;
+
+ /**
+ * Benchmark function.
+ *
+ * @private
+ * @param {Benchmark} b - benchmark instance
+ */
+ function benchmark( b ) {
+ var out;
+ var i;
+
+ b.tic();
+ for ( i = 0; i < b.iterations; i++ ) {
+ out = hasAlmostEqualValues( x, x, 1 );
+ if ( typeof out !== 'boolean' ) {
+ b.fail( 'should return a boolean' );
+ }
+ }
+ b.toc();
+ if ( !isBoolean( out ) ) {
+ b.fail( 'should return a boolean' );
+ }
+ b.pass( 'benchmark finished' );
+ b.end();
+ }
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+ var len;
+ var min;
+ var max;
+ var f;
+ var i;
+
+ min = 1; // 10^min
+ max = 6; // 10^max
+
+ for ( i = min; i <= max; i++ ) {
+ len = pow( 10, i );
+
+ f = createBenchmark( len );
+ bench( pkg+':dtype=generic,len='+len, f );
+ }
+}
+
+main();
diff --git a/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/docs/repl.txt b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/docs/repl.txt
new file mode 100644
index 000000000000..98404e144b7d
--- /dev/null
+++ b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/docs/repl.txt
@@ -0,0 +1,33 @@
+
+{{alias}}( x, y, maxULP )
+ Tests if two arrays have respective elements which are approximately equal
+ within a specified number of ULPs (units in the last place).
+
+ If provided arrays of unequal length, the function returns `false`.
+
+ Parameters
+ ----------
+ x: Array|TypedArray|Object
+ First input array.
+
+ y: Array|TypedArray|Object
+ Second input array.
+
+ maxULP: integer
+ Maximum allowed ULP difference.
+
+ Returns
+ -------
+ bool: boolean
+ Boolean indicating whether two arrays are approximately equal.
+
+ Examples
+ --------
+ > var x = [ 0, 0, 1, 0 ];
+ > var y = [ 0, 0, 1, 0 ];
+ > var out = {{alias}}( x, y, 0 )
+ true
+
+ See Also
+ --------
+
diff --git a/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/docs/types/index.d.ts b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/docs/types/index.d.ts
new file mode 100644
index 000000000000..cb6bbab2d4e1
--- /dev/null
+++ b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/docs/types/index.d.ts
@@ -0,0 +1,49 @@
+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+// TypeScript Version: 4.1
+
+///
+
+import { Collection, AccessorArrayLike } from '@stdlib/types/array';
+
+/**
+* Tests if two arrays have respective elements which are approximately equal within a specified number of ULPs (units in the last place).
+*
+* ## Notes
+*
+* - If provided arrays of unequal length, the function returns `false`.
+*
+* @param x - first input array
+* @param y - second input array
+* @param maxULP - maximum allowed ULP difference
+* @returns boolean indicating whether both arrays are approximately equal
+*
+* @example
+* var x = [ 0, 0, 1, 0 ];
+* var y = [ 0, 0, 1, 0 ];
+*
+* var out = hasAlmostEqualValues( x, y, 0 );
+* // returns true
+*/
+declare function hasAlmostEqualValues( x: Collection | AccessorArrayLike, y: Collection | AccessorArrayLike, maxULP: number ): boolean;
+
+
+// EXPORTS //
+
+export = hasAlmostEqualValues;
diff --git a/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/docs/types/test.ts b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/docs/types/test.ts
new file mode 100644
index 000000000000..84ef3c884d57
--- /dev/null
+++ b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/docs/types/test.ts
@@ -0,0 +1,84 @@
+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+import toAccessorArray = require( '@stdlib/array/base/to-accessor-array' );
+import hasAlmostEqualValues = require( './index' );
+
+
+// TESTS //
+
+// The function returns a boolean...
+{
+ const x = [ 1, 2, 3 ];
+
+ hasAlmostEqualValues( x, x ); // $ExpectType boolean
+ hasAlmostEqualValues( new Float64Array( x ), new Float64Array( x ), 0 ); // $ExpectType boolean
+ hasAlmostEqualValues( new Float32Array( x ), new Float32Array( x ), 0 ); // $ExpectType boolean
+ hasAlmostEqualValues( new Int32Array( x ), new Int32Array( x ), 0 ); // $ExpectType boolean
+ hasAlmostEqualValues( new Int16Array( x ), new Int16Array( x ), 0 ); // $ExpectType boolean
+ hasAlmostEqualValues( new Int8Array( x ), new Int8Array( x ), 0 ); // $ExpectType boolean
+ hasAlmostEqualValues( new Uint32Array( x ), new Uint32Array( x ), 0 ); // $ExpectType boolean
+ hasAlmostEqualValues( new Uint16Array( x ), new Uint16Array( x ), 0 ); // $ExpectType boolean
+ hasAlmostEqualValues( new Uint8Array( x ), new Uint8Array( x ), 0 ); // $ExpectType boolean
+ hasAlmostEqualValues( new Uint8ClampedArray( x ), new Uint8ClampedArray( x ), 0 ); // $ExpectType boolean
+ hasAlmostEqualValues( toAccessorArray( x ), toAccessorArray( x ), 0 ); // $ExpectType boolean
+}
+
+// The compiler throws an error if the function is provided a first argument which is not a collection...
+{
+ const x = [ 1, 2, 3 ];
+
+ hasAlmostEqualValues( 2, x, 0 ); // $ExpectError
+ hasAlmostEqualValues( false, x, 0 ); // $ExpectError
+ hasAlmostEqualValues( true, x, 0 ); // $ExpectError
+ hasAlmostEqualValues( {}, x, 0 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a second argument which is not a collection...
+{
+ const x = [ 1, 2, 3 ];
+
+ hasAlmostEqualValues( x, 2, 0 ); // $ExpectError
+ hasAlmostEqualValues( x, false, 0 ); // $ExpectError
+ hasAlmostEqualValues( x, true, 0 ); // $ExpectError
+ hasAlmostEqualValues( x, {}, 0 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a third argument which is not a number...
+{
+ const x = [ 1, 2, 3 ];
+
+ hasAlmostEqualValues( x, x, '2' ); // $ExpectError
+ hasAlmostEqualValues( x, x, false ); // $ExpectError
+ hasAlmostEqualValues( x, x, true ); // $ExpectError
+ hasAlmostEqualValues( x, x, null ); // $ExpectError
+ hasAlmostEqualValues( x, x, undefined ); // $ExpectError
+ hasAlmostEqualValues( x, x, {} ); // $ExpectError
+ hasAlmostEqualValues( x, x, [] ); // $ExpectError
+ hasAlmostEqualValues( x, x, ( x: number ): number => x ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided an unsupported number of arguments...
+{
+ const x = [ 1, 2, 3 ];
+
+ hasAlmostEqualValues(); // $ExpectError
+ hasAlmostEqualValues( x ); // $ExpectError
+ hasAlmostEqualValues( x, x ); // $ExpectError
+ hasAlmostEqualValues( x, x, 0, 1 ); // $ExpectError
+}
diff --git a/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/examples/index.js b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/examples/index.js
new file mode 100644
index 000000000000..85f8b60eacee
--- /dev/null
+++ b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/examples/index.js
@@ -0,0 +1,38 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+var uniform = require( '@stdlib/random/array/uniform' );
+var Complex128Array = require( '@stdlib/array/complex128' );
+var hasAlmostEqualValues = require( './../lib' );
+
+var buf = uniform( 10, 0, 10 );
+// returns
+
+var x = new Complex128Array( buf );
+// returns
+
+var y = new Complex128Array( buf );
+// returns
+
+var out = hasAlmostEqualValues( x, y, 1 );
+// returns true
+
+console.log( out );
+// => true
diff --git a/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/lib/index.js b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/lib/index.js
new file mode 100644
index 000000000000..09f65b749257
--- /dev/null
+++ b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/lib/index.js
@@ -0,0 +1,43 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+/**
+* Test if two arrays have respective elements which are approximately equal within a specified number of ULPs (units in the last place).
+*
+* @module @stdlib/array/base/assert/has-almost-equal-values
+*
+* @example
+* var hasAlmostEqualValues = require( '@stdlib/array/base/assert/has-almost-equal-values' );
+*
+* var x = [ 0, 0, 1, 0 ];
+* var y = [ 0, 0, 1, 0 ];
+*
+* var out = hasAlmostEqualValues( x, y, 1 );
+* // returns true
+*/
+
+// MODULES //
+
+var main = require( './main.js' );
+
+
+// EXPORTS //
+
+module.exports = main;
diff --git a/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/lib/main.js b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/lib/main.js
new file mode 100644
index 000000000000..fa756327df5b
--- /dev/null
+++ b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/lib/main.js
@@ -0,0 +1,209 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var isComplex128Array = require( '@stdlib/array/base/assert/is-complex128array' );
+var isComplex64Array = require( '@stdlib/array/base/assert/is-complex64array' );
+var isBooleanArray = require( '@stdlib/array/base/assert/is-booleanarray' );
+var arraylike2object = require( '@stdlib/array/base/arraylike2object' );
+var reinterpret128 = require( '@stdlib/strided/base/reinterpret-complex128' );
+var reinterpret64 = require( '@stdlib/strided/base/reinterpret-complex64' );
+var reinterpretBoolean = require( '@stdlib/strided/base/reinterpret-boolean' );
+var isAlmostEqualF32 = require( '@stdlib/number/float32/base/assert/is-almost-equal' );
+var isAlmostEqualF64 = require( '@stdlib/number/float64/base/assert/is-almost-equal' );
+var isAlmostEqual = require( '@stdlib/assert/is-almost-equal' );
+
+
+// FUNCTIONS //
+
+/**
+* Tests if two arrays have respective elements which are approximately equal within a specified number of ULPs (units in the last place).
+*
+* @private
+* @param {Collection} x - first input array
+* @param {Collection} y - second input array
+* @param {NonNegativeInteger} maxULP - maximum allowed ULP difference
+* @param {Function} fcn - function which tests approximate equality
+* @returns {boolean} boolean indicating if both arrays have respective elements which are approximately equal
+*
+* @example
+* var base = require( '@stdlib/number/float64/base/assert/is-almost-equal' );
+*
+* var x = [ 0, 0, 1, 0 ];
+* var y = [ 0, 0, 1, 0 ];
+*
+* var out = internal( x, y, 1, base );
+* // returns true
+*
+* @example
+* var base = require( '@stdlib/number/float64/base/assert/is-almost-equal' );
+*
+* var x = [ 0, 0, 0, 0 ];
+* var y = [ 0, 0, 1, 0 ];
+*
+* var out = internal( x, y, 1, base );
+* // returns false
+*/
+function internal( x, y, maxULP, fcn ) {
+ var i;
+ for ( i = 0; i < x.length; i++ ) {
+ if ( !fcn( x[ i ], y[ i ], maxULP ) ) {
+ return false;
+ }
+ }
+ return true;
+}
+
+/**
+* Tests if two arrays have respective elements which are approximately equal within a specified number of ULPs (units in the last place).
+*
+* @private
+* @param {Object} x - first input array object
+* @param {Object} y - second input array object
+* @param {NonNegativeInteger} maxULP - maximum allowed ULP difference
+* @param {Function} fcn - function which tests approximate equality
+* @returns {boolean} boolean indicating if both arrays have respective elements which are approximately equal
+*
+* @example
+* var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' );
+* var arraylike2object = require( '@stdlib/array/base/arraylike2object' );
+* var assert = require( '@stdlib/assert/is-almost-equal' );
+*
+* var x = arraylike2object( toAccessorArray( [ 0, 0, 1, 0 ] ) );
+* var y = arraylike2object( toAccessorArray( [ 0, 0, 1, 0 ] ) );
+*
+* var out = accessors( x, y, 0, assert );
+* // returns true
+*
+* @example
+* var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' );
+* var arraylike2object = require( '@stdlib/array/base/arraylike2object' );
+* var assert = require( '@stdlib/assert/is-almost-equal' );
+*
+* var x = arraylike2object( toAccessorArray( [ 0, 0, 0, 0 ] ) );
+* var y = arraylike2object( toAccessorArray( [ 0, 0, 1, 0 ] ) );
+*
+* var out = accessors( x, y, 1, assert);
+* // returns false
+*/
+function accessors( x, y, maxULP, fcn ) {
+ var xdata;
+ var ydata;
+ var xget;
+ var yget;
+ var i;
+
+ xdata = x.data;
+ ydata = y.data;
+
+ xget = x.accessors[ 0 ];
+ yget = y.accessors[ 0 ];
+
+ for ( i = 0; i < xdata.length; i++ ) {
+ if ( !fcn( xget( xdata, i ), yget( ydata, i ), maxULP ) ) {
+ return false;
+ }
+ }
+ return true;
+}
+
+
+// MAIN //
+
+/**
+* Tests if two arrays have respective elements which are approximately equal within a specified number of ULPs (units in the last place).
+*
+* @param {Collection} x - first input array
+* @param {Collection} y - second input array
+* @param {NonNegativeInteger} maxULP - maximum allowed ULP difference
+* @returns {boolean} boolean indicating if both arrays have respective elements which are approximately equal
+*
+* @example
+* var x = [ 0, 0, 1, 0 ];
+* var y = [ 0, 0, 1, 0 ];
+*
+* var out = hasAlmostEqualValues( x, y, 0 );
+* // returns true
+*
+* @example
+* var x = [ 0, 0, 0, 0 ];
+* var y = [ 0, 0, 1, 0 ];
+*
+* var out = hasAlmostEqualValues( x, y, 1 );
+* // returns false
+*/
+function hasAlmostEqualValues( x, y, maxULP ) {
+ var FLG;
+ var xo;
+ var yo;
+ var xr;
+ var yr;
+
+ if ( x.length !== y.length ) {
+ return false;
+ }
+ xo = arraylike2object( x );
+ yo = arraylike2object( y );
+ if ( xo.accessorProtocol || yo.accessorProtocol ) {
+ FLG = 2;
+
+ // If provided boolean arrays, reinterpret the arrays to avoid using accessors to access array elements...
+ if ( isBooleanArray( x ) ) {
+ if ( isBooleanArray( y ) ) {
+ return internal( reinterpretBoolean( x, 0 ), reinterpretBoolean( y, 0 ), maxULP, isAlmostEqualF64 ); // eslint-disable-line max-len
+ }
+ return accessors( xo, yo, maxULP, isAlmostEqual ); // general accessors should always use general comparison
+ }
+ // If provided a complex number array, reinterpret as a real typed array and test interleaved real and imaginary components...
+ if ( isComplex128Array( x ) ) {
+ xr = reinterpret128( x, 0 );
+ FLG -= 1;
+ } else if ( isComplex64Array( x ) ) {
+ xr = reinterpret64( x, 0 );
+ FLG -= 1;
+ }
+ if ( isComplex128Array( y ) ) {
+ yr = reinterpret128( y, 0 );
+ FLG -= 1;
+ } else if ( isComplex64Array( y ) ) {
+ yr = reinterpret64( y, 0 );
+ FLG -= 1;
+ }
+ if ( FLG === 0 ) {
+ // Determine whether we should use double-precision or single-precision comparison...
+ if ( xr.BYTES_PER_ELEMENT === 8 || yr.BYTES_PER_ELEMENT === 8 ) {
+ return internal( xr, yr, maxULP, isAlmostEqualF64 );
+ }
+ return internal( xr, yr, maxULP, isAlmostEqualF32 );
+ }
+ return accessors( xo, yo, maxULP, isAlmostEqual ); // general accessors should always use general comparison
+ }
+ // Determine whether we should use double-precision or single-precision comparison...
+ if ( x.BYTES_PER_ELEMENT === 4 && y.BYTES_PER_ELEMENT === 4 ) {
+ return internal( x, y, maxULP, isAlmostEqualF32 );
+ }
+ return internal( x, y, maxULP, isAlmostEqual ); // default to general comparison
+}
+
+
+// EXPORTS //
+
+module.exports = hasAlmostEqualValues;
diff --git a/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/package.json b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/package.json
new file mode 100644
index 000000000000..6dfa56daac3b
--- /dev/null
+++ b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/package.json
@@ -0,0 +1,69 @@
+{
+ "name": "@stdlib/array/base/assert/has-almost-equal-values",
+ "version": "0.0.0",
+ "description": "Test if two arrays have respective elements which are approximately equal within a specified number of ULPs (units in the last place).",
+ "license": "Apache-2.0",
+ "author": {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ },
+ "contributors": [
+ {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ }
+ ],
+ "main": "./lib",
+ "directories": {
+ "benchmark": "./benchmark",
+ "doc": "./docs",
+ "example": "./examples",
+ "lib": "./lib",
+ "test": "./test"
+ },
+ "types": "./docs/types",
+ "scripts": {},
+ "homepage": "https://github.com/stdlib-js/stdlib",
+ "repository": {
+ "type": "git",
+ "url": "git://github.com/stdlib-js/stdlib.git"
+ },
+ "bugs": {
+ "url": "https://github.com/stdlib-js/stdlib/issues"
+ },
+ "dependencies": {},
+ "devDependencies": {},
+ "engines": {
+ "node": ">=0.10.0",
+ "npm": ">2.7.0"
+ },
+ "os": [
+ "aix",
+ "darwin",
+ "freebsd",
+ "linux",
+ "macos",
+ "openbsd",
+ "sunos",
+ "win32",
+ "windows"
+ ],
+ "keywords": [
+ "stdlib",
+ "stdtypes",
+ "types",
+ "data",
+ "structure",
+ "array",
+ "generic",
+ "test",
+ "assert",
+ "validate",
+ "same",
+ "deep",
+ "equal",
+ "equality",
+ "compare",
+ "comparison"
+ ]
+}
diff --git a/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/test/test.js b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/test/test.js
new file mode 100644
index 000000000000..b579b7240f3b
--- /dev/null
+++ b/lib/node_modules/@stdlib/array/base/assert/has-almost-equal-values/test/test.js
@@ -0,0 +1,529 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var tape = require( 'tape' );
+var FLOAT64_EPS = require( '@stdlib/constants/float64/eps' );
+var FLOAT32_EPS = require( '@stdlib/constants/float32/eps' );
+var AccessorArray = require( '@stdlib/array/base/accessor' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Float32Array = require( '@stdlib/array/float32' );
+var Complex64Array = require( '@stdlib/array/complex64' );
+var Complex128Array = require( '@stdlib/array/complex128' );
+var BooleanArray = require( '@stdlib/array/bool' );
+var hasAlmostEqualValues = require( './../lib' );
+
+
+// TESTS //
+
+tape( 'main export is a function', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof hasAlmostEqualValues, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'if provided collections of unequal length, the function returns `false`', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = [];
+ y = [ 1, 2, 3 ];
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ x = [ 1, 2 ];
+ y = [ 1, 2, 3 ];
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ x = new Float64Array( [ 1 ] );
+ y = [];
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'if provided empty collections, the function returns `true` (generic)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = [];
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = [];
+ y = [];
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'if provided empty collections, the function returns `true` (real typed array)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = new Float64Array( [] );
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Float64Array( [] );
+ y = new Float64Array( [] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'if provided empty collections, the function returns `true` (mixed)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = [];
+ y = new Float64Array( [] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Float64Array( [] );
+ y = [];
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new BooleanArray( [] );
+ y = [];
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = [];
+ y = new BooleanArray( [] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'if provided empty collections, the function returns `true` (boolean array)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = new BooleanArray( [] );
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new BooleanArray( [] );
+ y = new BooleanArray( [] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'if provided empty collections, the function returns `true` (complex typed array)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = new Complex64Array( [] );
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Complex64Array( [] );
+ y = new Complex64Array( [] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Complex128Array( [] );
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Complex128Array( [] );
+ y = new Complex128Array( [] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'if provided empty collections, the function returns `true` (accessor)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = new AccessorArray( [] );
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new AccessorArray( [] );
+ y = new AccessorArray( [] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `true` if both arrays have respective elements which are approximately equal (generic)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = [ 1, 0, 3 ];
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = [ 1, 0, 3 ];
+ y = [ 1, 0, 3 ];
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `true` if both arrays have respective elements which are approximately equal (real typed array)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = new Float64Array( [ 0.0, 2.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Float64Array( [ 0.0, 2.0, 0.0 ] );
+ y = new Float64Array( [ 0.0, 2.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Float64Array( [ 0.0, 2.0, 1.0+FLOAT64_EPS ] );
+ y = new Float64Array( [ 0.0, 2.0, 1.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Float32Array( [ 0.0, 2.0, 1.0+FLOAT32_EPS ] );
+ y = new Float32Array( [ 0.0, 2.0, 1.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `true` if both arrays have respective elements which are approximately equal (mixed)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ // Exactly equal...
+ x = [ 0.0, 2.0, 0.0 ];
+ y = new Float64Array( [ 0.0, 2.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Float32Array( [ 0.0, 2.0, 0.0 ] );
+ y = new Float64Array( [ 0.0, 2.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Float64Array( [ 0.0, 2.0, 0.0 ] );
+ y = [ 0.0, 2.0, 0.0 ];
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Float64Array( [ 0.0, 2.0, 0.0 ] );
+ y = new Float32Array( [ 0.0, 2.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new BooleanArray( [ true, false, true ] );
+ y = [ true, false, true ];
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = [ true, false, true ];
+ y = new BooleanArray( [ true, false, true ] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ // Approximately equal...
+ x = [ 0.0, 2.0, 1.0 ];
+ y = new Float64Array( [ 0.0, 2.0, 1.0+FLOAT64_EPS ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Float64Array( [ 0.0, 2.0, 1.0 ] );
+ y = [ 0.0, 2.0, 1.0+FLOAT64_EPS ];
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Float64Array( [ 0.0, 2.0, 1.0+FLOAT64_EPS ] );
+ y = new Float32Array( [ 0.0, 2.0, 1.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `true` if both arrays have respective elements which are approximately equal (boolean array)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = new BooleanArray( [ true, false, true ] );
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new BooleanArray( [ true, false, true ] );
+ y = new BooleanArray( [ true, false, true ] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `true` if both arrays have respective elements which are approximately equal (complex typed array)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = new Complex64Array( [ 0.0, 2.0, 0.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Complex64Array( [ 0.0, 2.0, 0.0, 0.0 ] );
+ y = new Complex64Array( [ 0.0, 2.0, 0.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Complex128Array( [ 0.0, 0.0, 3.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Complex128Array( [ 0.0, 0.0, 3.0, 0.0 ] );
+ y = new Complex128Array( [ 0.0, 0.0, 3.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Complex128Array( [ 0.0, 0.0, 3.0, 0.0 ] );
+ y = new Complex64Array( [ 0.0, 0.0, 3.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Complex64Array( [ 0.0, 0.0, 3.0, 0.0 ] );
+ y = new Complex128Array( [ 0.0, 0.0, 3.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ // Approximately equal...
+ x = new Complex128Array( [ 0.0, 1.0, 3.0, 0.0 ] );
+ y = new Complex128Array( [ 0.0, 1.0+FLOAT64_EPS, 3.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Complex128Array( [ 0.0, 1.0+FLOAT64_EPS, 3.0, 0.0 ] );
+ y = new Complex128Array( [ 0.0, 1.0, 3.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Complex64Array( [ 0.0, 1.0, 3.0, 0.0 ] );
+ y = new Complex128Array( [ 0.0, 1.0+FLOAT64_EPS, 3.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new Complex64Array( [ 0.0, 1.0, 3.0, 0.0 ] );
+ y = new Complex64Array( [ 0.0, 1.0+FLOAT32_EPS, 3.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `true` if both arrays have respective elements which are approximately equal (accessor)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = new AccessorArray( [ 0, 2, 3 ] );
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = new AccessorArray( [ 0, 2, 3 ] );
+ y = new AccessorArray( [ 0, 2, 3 ] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `true` if both arrays have respective elements which are approximately equal (array-like object)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = {
+ 'length': 3,
+ '0': 1,
+ '1': 0,
+ '2': 0
+ };
+ out = hasAlmostEqualValues( x, x, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ x = {
+ 'length': 3,
+ '0': 1,
+ '1': 0,
+ '2': 0
+ };
+ y = {
+ 'length': 3,
+ '0': 1,
+ '1': 0,
+ '2': 0
+ };
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `false` if both arrays do not have respective elements which are approximately equal (generic)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = [ 0, 0, 0 ];
+ y = [ 0, 1, 0 ];
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `false` if both arrays do not have respective elements which are approximately equal (real typed array)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = new Float64Array( [ 0.0, 0.0, 0.0 ] );
+ y = new Float64Array( [ 0.0, 0.0, 1.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ x = new Float64Array( [ 0.0, 0.0, 1.0+FLOAT64_EPS+FLOAT64_EPS ] );
+ y = new Float64Array( [ 0.0, 0.0, 1.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `false` if both arrays do not have respective elements which are approximately equal (mixed)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = [ 0.0, 0.0, 0.0 ];
+ y = new Float64Array( [ 0.0, 0.0, 1.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ x = new Float64Array( [ 0.0, 0.0, 1.0 ] );
+ y = [ 0.0, 0.0, 0.0 ];
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ x = new Float64Array( [ 0.0, 0.0, 1.0, 0.0 ] );
+ y = new Complex128Array( [ 0.0, 0.0, 0.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `false` if both arrays do not have respective elements which are approximately equal (boolean array)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = new BooleanArray( [ true, false, false, true ] );
+ y = new BooleanArray( [ true, true, false, false ] );
+ out = hasAlmostEqualValues( x, y, 0 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `false` if both arrays do not have respective elements which are approximately equal (complex typed array)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = new Complex64Array( [ 0.0, 0.0, 0.0, 0.0 ] );
+ y = new Complex64Array( [ 0.0, 0.0, 0.0, 1.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ x = new Complex128Array( [ 0.0, 0.0, 0.0, 0.0 ] );
+ y = new Complex128Array( [ 1.0, 0.0, 0.0, 0.0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `false` if both arrays do not have respective elements which are approximately equal (accessor)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = new AccessorArray( [ 0, 0, 1 ] );
+ y = new AccessorArray( [ 0, 0, 0 ] );
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `false` if both arrays do not have respective elements which are approximately equal (array-like object)', function test( t ) {
+ var out;
+ var x;
+ var y;
+
+ x = {
+ 'length': 3,
+ '0': 0,
+ '1': 1,
+ '2': 0
+ };
+ y = {
+ 'length': 3,
+ '0': 0,
+ '1': 0,
+ '2': 0
+ };
+ out = hasAlmostEqualValues( x, y, 1 );
+ t.strictEqual( out, false, 'returns expected value' );
+
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/array/base/assert/lib/index.js b/lib/node_modules/@stdlib/array/base/assert/lib/index.js
index cde6bf937a63..b696b51281de 100644
--- a/lib/node_modules/@stdlib/array/base/assert/lib/index.js
+++ b/lib/node_modules/@stdlib/array/base/assert/lib/index.js
@@ -45,6 +45,15 @@ var ns = {};
*/
setReadOnly( ns, 'contains', require( '@stdlib/array/base/assert/contains' ) );
+/**
+* @name hasAlmostEqualValues
+* @memberof ns
+* @readonly
+* @type {Function}
+* @see {@link module:@stdlib/array/base/assert/has-almost-equal-values}
+*/
+setReadOnly( ns, 'hasAlmostEqualValues', require( '@stdlib/array/base/assert/has-almost-equal-values' ) );
+
/**
* @name hasEqualValues
* @memberof ns
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/README.md b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/README.md
new file mode 100644
index 000000000000..6fdb33ccd0ca
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/README.md
@@ -0,0 +1,117 @@
+
+
+# isAlmostEqualFloat64Array
+
+> Test if two arguments are both [Float64Arrays][@stdlib/array/float64] and contain respective elements which are [approximately equal][@stdlib/assert/is-almost-equal] within a specified number of ULPs (units in the last place).
+
+
+
+## Usage
+
+```javascript
+var isAlmostEqualFloat64Array = require( '@stdlib/assert/is-almost-equal-float64array' );
+```
+
+#### isAlmostEqualFloat64Array( v1, v2, maxULP )
+
+Tests if two arguments are both [Float64Arrays][@stdlib/array/float64] and contain respective elements which are [approximately equal][@stdlib/assert/is-almost-equal] within a specified number of ULPs (units in the last place).
+
+```javascript
+var EPS = require( '@stdlib/constants/float64/eps' );
+var Float64Array = require( '@stdlib/array/float64' );
+
+var x = new Float64Array( [ 1.0, 2.0 ] );
+var y = new Float64Array( [ 1.0+EPS, 2.0 ] );
+
+var bool = isAlmostEqualFloat64Array( x, y, 0 );
+// returns false
+
+bool = isAlmostEqualFloat64Array( x, y, 1 );
+// returns true
+
+bool = isAlmostEqualFloat64Array( x, [ 1.0, 2.0 ], 1 );
+// returns false
+```
+
+
+
+
+
+
+
+## Notes
+
+- The function returns `false` if either input value is a `Float64Array` containing `NaN`.
+- The function does not distinguish between `-0` and `+0`, treating them as equal.
+
+
+
+
+
+
+
+## Examples
+
+
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+var isAlmostEqualFloat64Array = require( '@stdlib/assert/is-almost-equal-float64array' );
+
+var x = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+var y = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+var out = isAlmostEqualFloat64Array( x, y, 0 );
+// returns true
+
+x = new Float64Array( [ -0.0, 0.0, -0.0 ] );
+y = new Float64Array( [ 0.0, -0.0, 0.0 ] );
+out = isAlmostEqualFloat64Array( x, y, 1 );
+// returns true
+
+x = new Float64Array( [ NaN, NaN, NaN ] );
+y = new Float64Array( [ NaN, NaN, NaN ] );
+out = isAlmostEqualFloat64Array( x, y, 0 );
+// returns false
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+[@stdlib/array/float64]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/float64
+
+[@stdlib/assert/is-almost-equal]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/assert/is-almost-equal
+
+
+
+
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/benchmark/benchmark.length.js b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/benchmark/benchmark.length.js
new file mode 100644
index 000000000000..3b21aec4929c
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/benchmark/benchmark.length.js
@@ -0,0 +1,97 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var bench = require( '@stdlib/bench' );
+var isBoolean = require( '@stdlib/assert/is-boolean' ).isPrimitive;
+var pow = require( '@stdlib/math/base/special/pow' );
+var zeroTo = require( '@stdlib/array/base/zero-to' );
+var Float64Array = require( '@stdlib/array/float64' );
+var pkg = require( './../package.json' ).name;
+var isAlmostEqualFloat64Array = require( './../lib' );
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} len - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( len ) {
+ var x = new Float64Array( zeroTo( len ) );
+ var y = new Float64Array( zeroTo( len ) );
+ return benchmark;
+
+ /**
+ * Benchmark function.
+ *
+ * @private
+ * @param {Benchmark} b - benchmark instance
+ */
+ function benchmark( b ) {
+ var bool;
+ var i;
+
+ b.tic();
+ for ( i = 0; i < b.iterations; i++ ) {
+ bool = isAlmostEqualFloat64Array( x, y, 1 );
+ if ( typeof bool !== 'boolean' ) {
+ b.fail( 'should return a boolean' );
+ }
+ }
+ b.toc();
+ if ( !isBoolean( bool ) ) {
+ b.fail( 'should return a boolean' );
+ }
+ b.pass( 'benchmark finished' );
+ b.end();
+ }
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+ var len;
+ var min;
+ var max;
+ var f;
+ var i;
+
+ min = 1; // 10^min
+ max = 6; // 10^max
+
+ for ( i = min; i <= max; i++ ) {
+ len = pow( 10, i );
+ f = createBenchmark( len );
+ bench( pkg+':len='+len, f );
+ }
+}
+
+main();
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/docs/repl.txt b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/docs/repl.txt
new file mode 100644
index 000000000000..ad7896d850a6
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/docs/repl.txt
@@ -0,0 +1,43 @@
+
+{{alias}}( v1, v2, maxULP )
+ Tests if two arguments are both Float64Arrays and contain respective
+ elements which are approximately equal within a specified number of ULPs
+ (units in the last place).
+
+ The function returns `false` if either input value is a `Float64Array`
+ containing `NaN`.
+
+ The function does not distinguish between `-0` and `+0`, treating them as
+ equal.
+
+ Parameters
+ ----------
+ v1: any
+ First input value.
+
+ v2: any
+ Second input value.
+
+ maxULP: integer
+ Maximum allowed ULP difference.
+
+ Returns
+ -------
+ bool: boolean
+ Boolean indicating whether two arguments are approximately equal.
+
+ Examples
+ --------
+ > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, 2.0, 3.0 ] );
+ > var y = new {{alias:@stdlib/array/float64}}( [ 1.0, 2.0, 3.0 ] );
+ > var bool = {{alias}}( x, y, 1 )
+ true
+
+ > x = new {{alias:@stdlib/array/float64}}( [ NaN, NaN, NaN ] );
+ > y = new {{alias:@stdlib/array/float64}}( [ NaN, NaN, NaN ] );
+ > bool = {{alias}}( x, y, 1 )
+ false
+
+ See Also
+ --------
+
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/docs/types/index.d.ts b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/docs/types/index.d.ts
new file mode 100644
index 000000000000..d8fc14ff1fb3
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/docs/types/index.d.ts
@@ -0,0 +1,57 @@
+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+// TypeScript Version: 4.1
+
+/**
+* Tests if two arguments are both Float64Arrays and contain respective elements which are approximately equal within a specified number of ULPs (units in the last place).
+*
+* ## Notes
+*
+* - The function returns `false` if either input value is a `Float64Array` containing `NaN`.
+* - The function does not distinguish between `-0` and `+0`, treating them as equal.
+*
+* @param v1 - first input value
+* @param v2 - second input value
+* @param maxULP - maximum allowed ULP difference
+* @returns boolean indicating whether two arguments are approximately equal
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+*
+* var x = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+* var y = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+*
+* var out = isAlmostEqualFloat64Array( x, y, 0 );
+* // returns true
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+*
+* var x = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+* var y = new Float64Array( [ 1.0, 2.0, 4.0 ] );
+*
+* var out = isAlmostEqualFloat64Array( x, y, 1 );
+* // returns false
+*/
+declare function isAlmostEqualFloat64Array( v1: any, v2: any, maxULP: number ): boolean;
+
+
+// EXPORTS //
+
+export = isAlmostEqualFloat64Array;
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/docs/types/test.ts b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/docs/types/test.ts
new file mode 100644
index 000000000000..88b391539fa3
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/docs/types/test.ts
@@ -0,0 +1,49 @@
+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+import isAlmostEqualFloat64Array = require( './index' );
+
+
+// TESTS //
+
+// The function returns a boolean...
+{
+ isAlmostEqualFloat64Array( 3.14, 3.14, 1 ); // $ExpectType boolean
+ isAlmostEqualFloat64Array( null, null, 1 ); // $ExpectType boolean
+ isAlmostEqualFloat64Array( 'beep', 'boop', 1 ); // $ExpectType boolean
+}
+
+// The compiler throws an error if the function is provided a third argument which is not a number...
+{
+ isAlmostEqualFloat64Array( 3.14, 3.14, '1' ); // $ExpectError
+ isAlmostEqualFloat64Array( 3.14, 3.14, true ); // $ExpectError
+ isAlmostEqualFloat64Array( 3.14, 3.14, false ); // $ExpectError
+ isAlmostEqualFloat64Array( 3.14, 3.14, null ); // $ExpectError
+ isAlmostEqualFloat64Array( 3.14, 3.14, undefined ); // $ExpectError
+ isAlmostEqualFloat64Array( 3.14, 3.14, [] ); // $ExpectError
+ isAlmostEqualFloat64Array( 3.14, 3.14, {} ); // $ExpectError
+ isAlmostEqualFloat64Array( 3.14, 3.14, ( x: number ): number => x ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided an unsupported number of arguments...
+{
+ isAlmostEqualFloat64Array(); // $ExpectError
+ isAlmostEqualFloat64Array( 3.14 ); // $ExpectError
+ isAlmostEqualFloat64Array( 3.14, 3.14 ); // $ExpectError
+ isAlmostEqualFloat64Array( 'beep', 'beep', 2, {} ); // $ExpectError
+}
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/examples/index.js b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/examples/index.js
new file mode 100644
index 000000000000..45155e86f3ab
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/examples/index.js
@@ -0,0 +1,40 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+var Float64Array = require( '@stdlib/array/float64' );
+var isAlmostEqualFloat64Array = require( './../lib' );
+
+var x = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+var y = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+var out = isAlmostEqualFloat64Array( x, y, 0 );
+console.log( out );
+// => true
+
+x = new Float64Array( [ -0.0, 0.0, -0.0 ] );
+y = new Float64Array( [ 0.0, -0.0, 0.0 ] );
+out = isAlmostEqualFloat64Array( x, y, 1 );
+console.log( out );
+// => true
+
+x = new Float64Array( [ NaN, NaN, NaN ] );
+y = new Float64Array( [ NaN, NaN, NaN ] );
+out = isAlmostEqualFloat64Array( x, y, 0 );
+console.log( out );
+// => false
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/lib/index.js b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/lib/index.js
new file mode 100644
index 000000000000..fb7b775ecb9e
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/lib/index.js
@@ -0,0 +1,54 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+/**
+* Test if two arguments are both Float64Arrays and contain respective elements which are approximately equal within a specified number of ULPs (units in the last place).
+*
+* @module @stdlib/assert/is-almost-equal-float64array
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var isAlmostEqualFloat64Array = require( '@stdlib/assert/is-almost-equal-float64array' );
+*
+* var x = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+* var y = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+*
+* var out = isAlmostEqualFloat64Array( x, y, 1 );
+* // returns true
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var isAlmostEqualFloat64Array = require( '@stdlib/assert/is-almost-equal-float64array' );
+*
+* var x = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+* var y = new Float64Array( [ 1.0, 2.0, 4.0 ] );
+*
+* var out = isAlmostEqualFloat64Array( x, y, 1 );
+* // returns false
+*/
+
+// MODULES //
+
+var main = require( './main.js' );
+
+
+// EXPORTS //
+
+module.exports = main;
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/lib/main.js b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/lib/main.js
new file mode 100644
index 000000000000..5d79d4502cfa
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/lib/main.js
@@ -0,0 +1,67 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var isFloat64Array = require( '@stdlib/assert/is-float64array' );
+var hasAlmostEqualValues = require( '@stdlib/array/base/assert/has-almost-equal-values' );
+
+
+// MAIN //
+
+/**
+* Tests if two arguments are both Float64Arrays and contain respective elements which are approximately equal within a specified number of ULPs (units in the last place).
+*
+* ## Notes
+*
+* - The function returns `false` if either input value is a `Float64Array` containing `NaN`.
+* - The function does not distinguish between `-0` and `+0`, treating them as equal.
+*
+* @param {*} v1 - first value
+* @param {*} v2 - second value
+* @param {NonNegativeInteger} maxULP - maximum allowed ULP difference
+* @returns {boolean} boolean result
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+*
+* var x = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+* var y = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+*
+* var out = isAlmostEqualFloat64Array( x, y, 0 );
+* // returns true
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+*
+* var x = new Float64Array( [ 1.0, 2.0, 3.0 ] );
+* var y = new Float64Array( [ 1.0, 2.0, 4.0 ] );
+*
+* var out = isAlmostEqualFloat64Array( x, y, 1 );
+* // returns false
+*/
+function isAlmostEqualFloat64Array( v1, v2, maxULP ) {
+ return ( isFloat64Array( v1 ) && isFloat64Array( v2 ) && hasAlmostEqualValues( v1, v2, maxULP ) ); // eslint-disable-line max-len
+}
+
+
+// EXPORTS //
+
+module.exports = isAlmostEqualFloat64Array;
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/package.json b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/package.json
new file mode 100644
index 000000000000..bc975a93d74a
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/package.json
@@ -0,0 +1,77 @@
+{
+ "name": "@stdlib/assert/is-almost-equal-float64array",
+ "version": "0.0.0",
+ "description": "Test if two arguments are both Float64Arrays and contain respective elements which are approximately equal within a specified number of ULPs (units in the last place).",
+ "license": "Apache-2.0",
+ "author": {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ },
+ "contributors": [
+ {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ }
+ ],
+ "main": "./lib",
+ "directories": {
+ "benchmark": "./benchmark",
+ "doc": "./docs",
+ "example": "./examples",
+ "lib": "./lib",
+ "test": "./test"
+ },
+ "types": "./docs/types",
+ "scripts": {},
+ "homepage": "https://github.com/stdlib-js/stdlib",
+ "repository": {
+ "type": "git",
+ "url": "git://github.com/stdlib-js/stdlib.git"
+ },
+ "bugs": {
+ "url": "https://github.com/stdlib-js/stdlib/issues"
+ },
+ "dependencies": {},
+ "devDependencies": {},
+ "engines": {
+ "node": ">=0.10.0",
+ "npm": ">2.7.0"
+ },
+ "os": [
+ "aix",
+ "darwin",
+ "freebsd",
+ "linux",
+ "macos",
+ "openbsd",
+ "sunos",
+ "win32",
+ "windows"
+ ],
+ "keywords": [
+ "stdlib",
+ "stdassert",
+ "assertion",
+ "assert",
+ "utilities",
+ "utility",
+ "utils",
+ "util",
+ "equal",
+ "same",
+ "strict",
+ "is",
+ "issame",
+ "issamevalue",
+ "isequal",
+ "isstrictequal",
+ "type",
+ "check",
+ "valid",
+ "validate",
+ "test",
+ "typed",
+ "array",
+ "float64"
+ ]
+}
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/test/test.js b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/test/test.js
new file mode 100644
index 000000000000..fd71278d58b0
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal-float64array/test/test.js
@@ -0,0 +1,114 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var tape = require( 'tape' );
+var typedarray = require( '@stdlib/array/typed' );
+var EPS = require( '@stdlib/constants/float64/eps' );
+var isAlmostEqualFloat64Array = require( './../lib' );
+
+
+// TESTS //
+
+tape( 'main export is a function', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof isAlmostEqualFloat64Array, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'the function returns `true` if provided two Float64Arrays having almost equal values', function test( t ) {
+ var x;
+ var y;
+
+ x = typedarray( [ 1.0, 2.0, 3.0 ], 'float64' );
+ t.strictEqual( isAlmostEqualFloat64Array( x, x, 0 ), true, 'returns expected value' );
+
+ x = typedarray( [ 1.0, 2.0, 3.0 ], 'float64' );
+ y = typedarray( [ 1.0, 2.0, 3.0 ], 'float64' );
+ t.strictEqual( isAlmostEqualFloat64Array( x, y, 0 ), true, 'returns expected value' );
+
+ x = typedarray( [ -0.0, 0.0, -0.0 ], 'float64' );
+ y = typedarray( [ -0.0, 0.0, -0.0 ], 'float64' );
+ t.strictEqual( isAlmostEqualFloat64Array( x, y, 0 ), true, 'returns expected value' );
+
+ x = typedarray( [ 1.0, 2.0, 3.0 ], 'float64' );
+ y = typedarray( [ 1.0+EPS, 2.0, 3.0 ], 'float64' );
+ t.strictEqual( isAlmostEqualFloat64Array( x, y, 1 ), true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `false` if not provided two Float64Arrays having almost equal values', function test( t ) {
+ var x;
+ var y;
+ var i;
+
+ x = [
+ '',
+ 'beep',
+ 5,
+ 3.14,
+ -3.14,
+ 0.0,
+ -0.0,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {},
+ typedarray( 10, 'float32' ),
+ typedarray( 10, 'int32' ),
+ typedarray( 10, 'float64' ),
+ typedarray( [ 1.0, 2.0, 3.0 ], 'float64' ),
+ typedarray( [ -0.0, -0.0, -0.0 ], 'float64' ),
+ typedarray( [ 1.0, 2.0, 3.0 ], 'float64' ),
+ typedarray( [ NaN, NaN, NaN ], 'float64' )
+ ];
+ y = [
+ 'abc',
+ 'boop',
+ -5,
+ -3.14,
+ 3.14,
+ -0.0,
+ 0.0,
+ false,
+ true,
+ void 0,
+ null,
+ [],
+ {},
+ function noop() {},
+ typedarray( 10, 'float32' ),
+ typedarray( 10, 'int32' ),
+ typedarray( 10, 'float32' ),
+ typedarray( [ 2.0, 4.0, 6.0 ], 'float64' ),
+ typedarray( [ 0.0, 0.0, 1.0 ], 'float64' ),
+ typedarray( [ 1.0+EPS+EPS, 2.0, 3.0 ], 'float64' ),
+ typedarray( [ NaN, NaN, NaN ], 'float64' )
+ ];
+ for ( i = 0; i < x.length; i++ ) {
+ t.strictEqual( isAlmostEqualFloat64Array( x[ i ], y[ i ], 1 ), false, 'returns expected value when provided '+x[ i ]+' and '+y[ i ] );
+ }
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal/README.md b/lib/node_modules/@stdlib/assert/is-almost-equal/README.md
new file mode 100644
index 000000000000..ea87c2198d7a
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal/README.md
@@ -0,0 +1,172 @@
+
+
+# isAlmostEqual
+
+> Test if two arguments are approximately equal within a specified number of ULPs (units in the last place).
+
+
+
+## Usage
+
+```javascript
+var isAlmostEqual = require( '@stdlib/assert/is-almost-equal' );
+```
+
+#### isAlmostEqual( a, b, maxULP )
+
+Tests if two arguments are approximately equal within a specified number of ULPs (units in the last place).
+
+```javascript
+var EPS = require( '@stdlib/constants/float64/eps' );
+
+var bool = isAlmostEqual( 1.0, 1.0+EPS, 1 );
+// returns true
+
+bool = isAlmostEqual( '', '', 0 );
+// returns true
+
+bool = isAlmostEqual( {}, {}, 1 );
+// returns false
+```
+
+The function returns `false` if either input value is `NaN` or, in the case of [complex numbers][@stdlib/complex], if either the real or imaginary component is `NaN`.
+
+```javascript
+var Complex128 = require( '@stdlib/complex/float64/ctor' );
+
+var bool = isAlmostEqual( NaN, 1.0, 1 );
+// returns false
+
+bool = isAlmostEqual( NaN, NaN, 1 );
+// returns false
+
+var z1 = new Complex128( NaN, 3.0 );
+var z2 = new Complex128( 1.0, 3.0 );
+
+bool = isAlmostEqual( z1, z2, 1 );
+// returns false
+
+z1 = new Complex128( NaN, NaN );
+z2 = new Complex128( NaN, NaN );
+
+bool = isAlmostEqual( z1, z2, 1 );
+// returns false
+```
+
+The function does not distinguish between `-0` and `+0`, treating them as equal.
+
+```javascript
+var Complex128 = require( '@stdlib/complex/float64/ctor' );
+
+var bool = isAlmostEqual( 0.0, -0.0, 0 );
+// returns true
+
+var z1 = new Complex128( 0.0, 0.0 );
+var z2 = new Complex128( -0.0, -0.0 );
+
+bool = isAlmostEqual( z1, z2, 0 );
+// returns true
+```
+
+
+
+
+
+
+
+
+
+
+
+## Examples
+
+
+
+```javascript
+var EPS = require( '@stdlib/constants/float64/eps' );
+var Complex128 = require( '@stdlib/complex/float64/ctor' );
+var isAlmostEqual = require( '@stdlib/assert/is-almost-equal' );
+
+console.log( isAlmostEqual( true, true, 0 ) );
+// => true
+
+console.log( isAlmostEqual( true, false, 1 ) );
+// => false
+
+console.log( isAlmostEqual( 'beep', 'beep', 1 ) );
+// => true
+
+console.log( isAlmostEqual( 1.0, 1.0+EPS, 1 ) );
+// => true
+
+console.log( isAlmostEqual( null, null, 0 ) );
+// => true
+
+console.log( isAlmostEqual( 0.0, -0.0, 0 ) );
+// => true
+
+console.log( isAlmostEqual( NaN, NaN, 1 ) );
+// => false
+
+var z1 = new Complex128( 1.0, 3.0+EPS );
+var z2 = new Complex128( 1.0+EPS, 3.0 );
+console.log( isAlmostEqual( z1, z2, 1 ) );
+// => true
+
+console.log( isAlmostEqual( {}, {}, 1 ) );
+// => false
+
+console.log( isAlmostEqual( [], [], 1 ) );
+// => false
+
+console.log( isAlmostEqual( isAlmostEqual, isAlmostEqual, 0 ) );
+// => true
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+[@stdlib/complex]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/complex
+
+
+
+
+
+
+
+
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal/benchmark/benchmark.js b/lib/node_modules/@stdlib/assert/is-almost-equal/benchmark/benchmark.js
new file mode 100644
index 000000000000..b6b75a052451
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal/benchmark/benchmark.js
@@ -0,0 +1,70 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+/* eslint-disable no-empty-function */
+
+'use strict';
+
+// MODULES //
+
+var bench = require( '@stdlib/bench' );
+var isBoolean = require( '@stdlib/assert/is-boolean' ).isPrimitive;
+var pkg = require( './../package.json' ).name;
+var isAlmostEqual = require( './../lib' );
+
+
+// MAIN //
+
+bench( pkg, function benchmark( b ) {
+ var values;
+ var bool;
+ var v;
+ var i;
+
+ values = [
+ '',
+ '5',
+ 0,
+ 5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ new RegExp( '.*' ), // eslint-disable-line prefer-regex-literals
+ new Date(),
+ function noop() {}
+ ];
+
+ b.tic();
+ for ( i = 0; i < b.iterations; i++ ) {
+ v = values[ i%values.length ];
+ bool = isAlmostEqual( v, v, 1 );
+ if ( typeof bool !== 'boolean' ) {
+ b.fail( 'should return a boolean' );
+ }
+ }
+ b.toc();
+ if ( !isBoolean( bool ) ) {
+ b.fail( 'should return a boolean' );
+ }
+ b.pass( 'benchmark finished' );
+ b.end();
+});
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal/docs/repl.txt b/lib/node_modules/@stdlib/assert/is-almost-equal/docs/repl.txt
new file mode 100644
index 000000000000..c4c2108cae5d
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal/docs/repl.txt
@@ -0,0 +1,46 @@
+
+{{alias}}( a, b, maxULP )
+ Tests if two arguments are approximately equal within a specified number of
+ ULPs (units in the last place).
+
+ The function returns `false` if either input value is `NaN` or in the case
+ of complex numbers, if either the real or imaginary component is `NaN`.
+
+ The function does not distinguish between `-0` and `+0`, treating them as
+ equal.
+
+ Parameters
+ ----------
+ a: any
+ First input value.
+
+ b: any
+ Second input value.
+
+ maxULP: integer
+ Maximum allowed ULP difference.
+
+ Returns
+ -------
+ bool: boolean
+ Boolean indicating whether two arguments are approximately equal within
+ a specified number of ULPs.
+
+ Examples
+ --------
+ > var bool = {{alias}}( true, true, 0 )
+ true
+ > bool = {{alias}}( 1+{{alias:@stdlib/constants/float64/eps}}, 1, 1 )
+ true
+ > bool = {{alias}}( {}, {}, 1 )
+ false
+ > bool = {{alias}}( 0.0, -0.0, 0 )
+ true
+ > bool = {{alias}}( NaN, 1.0, 1 )
+ false
+ > bool = {{alias}}( NaN, NaN, 0 )
+ false
+
+ See Also
+ --------
+
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal/docs/types/index.d.ts b/lib/node_modules/@stdlib/assert/is-almost-equal/docs/types/index.d.ts
new file mode 100644
index 000000000000..dc7f5bc402d3
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal/docs/types/index.d.ts
@@ -0,0 +1,60 @@
+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+// TypeScript Version: 4.1
+
+/**
+* Tests if two arguments are approximately equal within a specified number of ULPs (units in the last place).
+*
+* ## Notes
+*
+* - The function returns `false` if either input value is `NaN` or in the case of complex numbers, if either the real or imaginary component is `NaN`.
+* - The function does not distinguish between `-0` and `+0`, treating them as equal.
+*
+* @param a - first input value
+* @param b - second input value
+* @param maxULP - maximum allowed ULP difference
+* @returns boolean indicating whether two arguments are approximately equal within a specified number of ULPs
+*
+* @example
+* var EPS = require( '@stdlib/constants/float64/eps' );
+*
+* var bool = isAlmostEqual( 1.0, 1.0+EPS, 0 );
+* // returns false
+*
+* bool = isAlmostEqual( 1.0, 1.0+EPS, 1 );
+* // returns true
+*
+* bool = isAlmostEqual( {}, {}, 0 );
+* // returns false
+*
+* bool = isAlmostEqual( -0.0, 0.0, 0 );
+* // returns true
+*
+* bool = isAlmostEqual( NaN, NaN, 0 );
+* // returns false
+*
+* bool = isAlmostEqual( [], [], 1 );
+* // returns false
+*/
+declare function isAlmostEqual( a: any, b: any, maxULP: number ): boolean;
+
+
+// EXPORTS //
+
+export = isAlmostEqual;
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal/docs/types/test.ts b/lib/node_modules/@stdlib/assert/is-almost-equal/docs/types/test.ts
new file mode 100644
index 000000000000..fad7a7df2ae5
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal/docs/types/test.ts
@@ -0,0 +1,49 @@
+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+import isAlmostEqual = require( './index' );
+
+
+// TESTS //
+
+// The function returns a boolean...
+{
+ isAlmostEqual( 3.14, 3.14, 1 ); // $ExpectType boolean
+ isAlmostEqual( null, null, 0 ); // $ExpectType boolean
+ isAlmostEqual( 'beep', 'boop', 2 ); // $ExpectType boolean
+}
+
+// The compiler throws an error if the function is provided a third argument which is not a number...
+{
+ isAlmostEqual( 3.14, 3.14, '1' ); // $ExpectError
+ isAlmostEqual( 3.14, 3.14, true ); // $ExpectError
+ isAlmostEqual( 3.14, 3.14, false ); // $ExpectError
+ isAlmostEqual( 3.14, 3.14, null ); // $ExpectError
+ isAlmostEqual( 3.14, 3.14, undefined ); // $ExpectError
+ isAlmostEqual( 3.14, 3.14, [] ); // $ExpectError
+ isAlmostEqual( 3.14, 3.14, {} ); // $ExpectError
+ isAlmostEqual( 3.14, 3.14, ( x: number ): number => x ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided an unsupported number of arguments...
+{
+ isAlmostEqual(); // $ExpectError
+ isAlmostEqual( 3.14 ); // $ExpectError
+ isAlmostEqual( 'beep', 'beep' ); // $ExpectError
+ isAlmostEqual( 3.14, 3.14, 1, 2 ); // $ExpectError
+}
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal/examples/index.js b/lib/node_modules/@stdlib/assert/is-almost-equal/examples/index.js
new file mode 100644
index 000000000000..ebb3aa2f1b44
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal/examples/index.js
@@ -0,0 +1,58 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+var EPS = require( '@stdlib/constants/float64/eps' );
+var Complex128 = require( '@stdlib/complex/float64/ctor' );
+var isAlmostEqual = require( './../lib' );
+
+console.log( isAlmostEqual( true, true, 0 ) );
+// => true
+
+console.log( isAlmostEqual( true, false, 1 ) );
+// => false
+
+console.log( isAlmostEqual( 'beep', 'beep', 1 ) );
+// => true
+
+console.log( isAlmostEqual( 1.0, 1.0+EPS, 1 ) );
+// => true
+
+console.log( isAlmostEqual( null, null, 0 ) );
+// => true
+
+console.log( isAlmostEqual( 0.0, -0.0, 0 ) );
+// => true
+
+console.log( isAlmostEqual( NaN, NaN, 1 ) );
+// => false
+
+var z1 = new Complex128( 1.0, 3.0+EPS );
+var z2 = new Complex128( 1.0+EPS, 3.0 );
+console.log( isAlmostEqual( z1, z2, 1 ) );
+// => true
+
+console.log( isAlmostEqual( {}, {}, 1 ) );
+// => false
+
+console.log( isAlmostEqual( [], [], 1 ) );
+// => false
+
+console.log( isAlmostEqual( isAlmostEqual, isAlmostEqual, 0 ) );
+// => true
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal/lib/index.js b/lib/node_modules/@stdlib/assert/is-almost-equal/lib/index.js
new file mode 100644
index 000000000000..05618eb24e37
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal/lib/index.js
@@ -0,0 +1,56 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+/**
+* Test if two arguments are approximately equal within a specified number of ULPs (units in the last place).
+*
+* @module @stdlib/assert/is-almost-equal
+*
+* @example
+* var EPS = require( '@stdlib/constants/float64/eps' );
+* var isAlmostEqual = require( '@stdlib/assert/is-almost-equal' );
+*
+* var bool = isAlmostEqual( 1.0, 1.0+EPS, 0 );
+* // returns false
+*
+* bool = isAlmostEqual( 1.0, 1.0+EPS, 1 );
+* // returns true
+*
+* bool = isAlmostEqual( {}, {}, 0 );
+* // returns false
+*
+* bool = isAlmostEqual( -0.0, 0.0, 0 );
+* // returns true
+*
+* bool = isAlmostEqual( NaN, NaN, 0 );
+* // returns false
+*
+* bool = isAlmostEqual( [], [], 1 );
+* // returns false
+*/
+
+// MODULES //
+
+var main = require( './main.js' );
+
+
+// EXPORTS //
+
+module.exports = main;
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal/lib/main.js b/lib/node_modules/@stdlib/assert/is-almost-equal/lib/main.js
new file mode 100644
index 000000000000..c5891d2091a9
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal/lib/main.js
@@ -0,0 +1,85 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var isAlmostEqualComplex64 = require( '@stdlib/complex/float32/base/assert/is-almost-equal' );
+var isAlmostEqualComplex128 = require( '@stdlib/complex/float64/base/assert/is-almost-equal' );
+var isNumber = require( '@stdlib/assert/is-number' ).isPrimitive;
+var isAlmostEqualF64 = require( '@stdlib/number/float64/base/assert/is-almost-equal' );
+var isComplexLike = require( '@stdlib/assert/is-complex-like' );
+
+
+// MAIN //
+
+/**
+* Tests if two arguments are approximately equal within a specified number of ULPs (units in the last place).
+*
+* ## Notes
+*
+* - The function returns `false` if either input value is `NaN` or, in the case of complex numbers, if either the real or imaginary component is `NaN`.
+* - The function does not distinguish between `-0` and `+0`, treating them as equal.
+*
+* @param {*} a - first input value
+* @param {*} b - second input value
+* @param {NonNegativeInteger} maxULP - maximum allowed ULP difference
+* @returns {boolean} boolean indicating whether two arguments are approximately equal within a specified number of ULPs
+*
+* @example
+* var EPS = require( '@stdlib/constants/float64/eps' );
+*
+* var bool = isAlmostEqual( 1.0, 1.0+EPS, 0 );
+* // returns false
+*
+* bool = isAlmostEqual( 1.0, 1.0+EPS, 1 );
+* // returns true
+*
+* bool = isAlmostEqual( {}, {}, 0 );
+* // returns false
+*
+* bool = isAlmostEqual( -0.0, 0.0, 0 );
+* // returns true
+*
+* bool = isAlmostEqual( NaN, NaN, 0 );
+* // returns false
+*
+* bool = isAlmostEqual( [], [], 1 );
+* // returns false
+*/
+function isAlmostEqual( a, b, maxULP ) {
+ if ( a === b ) {
+ return true;
+ }
+ if ( isNumber( a ) && isNumber( b ) ) {
+ return isAlmostEqualF64( a, b, maxULP );
+ }
+ if ( isComplexLike( a ) && isComplexLike( b ) ) {
+ if ( a.BYTES_PER_ELEMENT === 4 && b.BYTES_PER_ELEMENT === 4 ) {
+ return isAlmostEqualComplex64( a, b, maxULP );
+ }
+ return isAlmostEqualComplex128( a, b, maxULP );
+ }
+ return false;
+}
+
+
+// EXPORTS //
+
+module.exports = isAlmostEqual;
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal/package.json b/lib/node_modules/@stdlib/assert/is-almost-equal/package.json
new file mode 100644
index 000000000000..a556f5e58a48
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal/package.json
@@ -0,0 +1,73 @@
+{
+ "name": "@stdlib/assert/is-almost-equal",
+ "version": "0.0.0",
+ "description": "Test if two arguments are approximately equal within a specified number of ULPs (units in the last place).",
+ "license": "Apache-2.0",
+ "author": {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ },
+ "contributors": [
+ {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ }
+ ],
+ "main": "./lib",
+ "directories": {
+ "benchmark": "./benchmark",
+ "doc": "./docs",
+ "example": "./examples",
+ "lib": "./lib",
+ "test": "./test"
+ },
+ "types": "./docs/types",
+ "scripts": {},
+ "homepage": "https://github.com/stdlib-js/stdlib",
+ "repository": {
+ "type": "git",
+ "url": "git://github.com/stdlib-js/stdlib.git"
+ },
+ "bugs": {
+ "url": "https://github.com/stdlib-js/stdlib/issues"
+ },
+ "dependencies": {},
+ "devDependencies": {},
+ "engines": {
+ "node": ">=0.10.0",
+ "npm": ">2.7.0"
+ },
+ "os": [
+ "aix",
+ "darwin",
+ "freebsd",
+ "linux",
+ "macos",
+ "openbsd",
+ "sunos",
+ "win32",
+ "windows"
+ ],
+ "keywords": [
+ "stdlib",
+ "stdassert",
+ "assertion",
+ "assert",
+ "utilities",
+ "utility",
+ "utils",
+ "util",
+ "equal",
+ "same",
+ "strict",
+ "is",
+ "isequal",
+ "isalmostequal",
+ "isstrictequal",
+ "type",
+ "check",
+ "valid",
+ "validate",
+ "test"
+ ]
+}
diff --git a/lib/node_modules/@stdlib/assert/is-almost-equal/test/test.js b/lib/node_modules/@stdlib/assert/is-almost-equal/test/test.js
new file mode 100644
index 000000000000..bb41d61ac875
--- /dev/null
+++ b/lib/node_modules/@stdlib/assert/is-almost-equal/test/test.js
@@ -0,0 +1,194 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+/* eslint-disable no-new-wrappers */
+
+'use strict';
+
+// MODULES //
+
+var tape = require( 'tape' );
+var EPS_F64 = require( '@stdlib/constants/float64/eps' );
+var EPS_F32 = require( '@stdlib/constants/float32/eps' );
+var Number = require( '@stdlib/number/ctor' );
+var Boolean = require( '@stdlib/boolean/ctor' );
+var Complex128 = require( '@stdlib/complex/float64/ctor' );
+var Complex64 = require( '@stdlib/complex/float32/ctor' );
+var isAlmostEqual = require( './../lib' );
+
+
+// TESTS //
+
+tape( 'main export is a function', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof isAlmostEqual, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'the function returns `true` if provided two arguments which are the same value irrespective of the specified number of ULPs', function test( t ) {
+ var values;
+ var i;
+
+ values = [
+ '',
+ 'beep',
+ 5,
+ 3.14,
+ -3.14,
+ 0.0,
+ -0.0,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {},
+ new Date(),
+ /.*/,
+ new Complex128( 5.0, 3.0 ),
+ new Complex64( 5.0, 2.0 )
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.strictEqual( isAlmostEqual( values[ i ], values[ i ], 0 ), true, 'returns expected value' );
+ t.strictEqual( isAlmostEqual( values[ i ], values[ i ], 1 ), true, 'returns expected value' );
+ }
+
+ t.strictEqual( isAlmostEqual( new Complex128( 5.0, 3.0 ), new Complex128( 5.0, 3.0 ), 0 ), true, 'returns expected value' );
+ t.strictEqual( isAlmostEqual( new Complex128( 5.0, 3.0 ), new Complex128( 5.0, 3.0 ), 1 ), true, 'returns expected value' );
+
+ t.strictEqual( isAlmostEqual( new Complex64( 5.0, 3.0 ), new Complex64( 5.0, 3.0 ), 0 ), true, 'returns expected value' );
+ t.strictEqual( isAlmostEqual( new Complex64( 5.0, 3.0 ), new Complex64( 5.0, 3.0 ), 1 ), true, 'returns expected value' );
+
+ t.strictEqual( isAlmostEqual( new Complex64( 5.0, 3.0 ), new Complex128( 5.0, 3.0 ), 0 ), true, 'returns expected value' );
+ t.strictEqual( isAlmostEqual( new Complex64( 5.0, 3.0 ), new Complex128( 5.0, 3.0 ), 1 ), true, 'returns expected value' );
+
+ t.strictEqual( isAlmostEqual( new Complex128( 5.0, 3.0 ), new Complex64( 5.0, 3.0 ), 0 ), true, 'returns expected value' );
+ t.strictEqual( isAlmostEqual( new Complex128( 5.0, 3.0 ), new Complex64( 5.0, 3.0 ), 1 ), true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `true` if provided two arguments which are approximately equal within a specified number of ULPs', function test( t ) {
+ t.strictEqual( isAlmostEqual( 1.0, 1.0+EPS_F64, 1 ), true, 'returns expected value' );
+ t.strictEqual( isAlmostEqual( 1.0+EPS_F64, 1.0, 1 ), true, 'returns expected value' );
+ t.strictEqual( isAlmostEqual( 1.0, 1.0+EPS_F64+EPS_F64, 2 ), true, 'returns expected value' );
+
+ t.strictEqual( isAlmostEqual( new Complex128( 1.0, 3.0 ), new Complex128( 1.0+EPS_F64, 3.0 ), 1 ), true, 'returns expected value' );
+ t.strictEqual( isAlmostEqual( new Complex64( 1.0, 3.0 ), new Complex64( 1.0+EPS_F32, 3.0 ), 1 ), true, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `false` if provided two arguments which are not approximately equal within a specified number of ULPs', function test( t ) {
+ var a;
+ var b;
+ var i;
+
+ a = [
+ '',
+ 'beep',
+ new String( 'beep' ),
+ 5,
+ 3.14,
+ -3.14,
+ new Number( 5 ),
+ true,
+ false,
+ new Boolean( true ),
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {},
+ new Date(),
+ /.*/,
+ new Complex128( 5.0, 3.0 ),
+ new Complex64( 5.0, 2.0 )
+ ];
+ b = [
+ 'abc',
+ 'boop',
+ new String( 'beep' ),
+ -5,
+ -3.14,
+ 3.14,
+ new Number( 5 ),
+ false,
+ true,
+ new Boolean( true ),
+ void 0,
+ null,
+ [],
+ {},
+ function noop() {},
+ new Date(),
+ /.*/,
+ new Complex128( -5.0, 3.0 ),
+ new Complex64( 5.0, -2.0 )
+ ];
+ for ( i = 0; i < a.length; i++ ) {
+ t.strictEqual( isAlmostEqual( a[ i ], b[ i ], 1 ), false, 'returns expected value' );
+ }
+
+ t.strictEqual( isAlmostEqual( 1.0, 1.0+EPS_F64+EPS_F64, 1 ), false, 'returns expected value' );
+ t.strictEqual( isAlmostEqual( 1.0+EPS_F64+EPS_F64, 1.0, 1 ), false, 'returns expected value' );
+
+ t.strictEqual( isAlmostEqual( new Complex128( 1.0, 3.0 ), new Complex128( 1.0+EPS_F64+EPS_F64, 3.0 ), 1 ), false, 'returns expected value' );
+ t.strictEqual( isAlmostEqual( new Complex64( 1.0, 3.0 ), new Complex64( 1.0+EPS_F32+EPS_F32, 3.0 ), 1 ), false, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function returns `false` if either input value is `NaN` or, in the case of complex numbers, if either the real or imaginary component is `NaN`', function test( t ) {
+ var a;
+ var b;
+ var i;
+
+ a = [
+ NaN,
+ NaN,
+ new Complex128( NaN, 3.0 ),
+ new Complex64( 5.0, NaN ),
+ new Complex128( NaN, NaN ),
+ new Complex64( NaN, NaN )
+ ];
+ b = [
+ NaN,
+ 1.0,
+ new Complex128( 1.0, 2.0 ),
+ new Complex64( 3.0, 4.0 ),
+ new Complex128( NaN, NaN ),
+ new Complex64( NaN, NaN )
+ ];
+ for ( i = 0; i < a.length; i++ ) {
+ t.strictEqual( isAlmostEqual( a[ i ], b[ i ], 1 ), false, 'returns expected value' );
+ }
+
+ t.end();
+});
+
+tape( 'the function does not distinguish between `-0` and `+0`', function test( t ) {
+ t.strictEqual( isAlmostEqual( -0.0, 0.0, 0 ), true, 'returns expected value' );
+ t.strictEqual( isAlmostEqual( 0.0, -0.0, 0 ), true, 'returns expected value' );
+
+ t.strictEqual( isAlmostEqual( new Complex128( -0.0, 3.0 ), new Complex128( 0.0, 3.0 ), 1 ), true, 'returns expected value' );
+ t.strictEqual( isAlmostEqual( new Complex64( -0.0, 3.0 ), new Complex64( 0.0, 3.0 ), 1 ), true, 'returns expected value' );
+
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/assert/lib/index.js b/lib/node_modules/@stdlib/assert/lib/index.js
index 706e42a1ca8f..3549942acbd8 100644
--- a/lib/node_modules/@stdlib/assert/lib/index.js
+++ b/lib/node_modules/@stdlib/assert/lib/index.js
@@ -486,6 +486,24 @@ setReadOnly( ns, 'isAccessorProperty', require( '@stdlib/assert/is-accessor-prop
*/
setReadOnly( ns, 'isAccessorPropertyIn', require( '@stdlib/assert/is-accessor-property-in' ) );
+/**
+* @name isAlmostEqual
+* @memberof ns
+* @readonly
+* @type {Function}
+* @see {@link module:@stdlib/assert/is-almost-equal}
+*/
+setReadOnly( ns, 'isAlmostEqual', require( '@stdlib/assert/is-almost-equal' ) );
+
+/**
+* @name isAlmostEqualFloat64Array
+* @memberof ns
+* @readonly
+* @type {Function}
+* @see {@link module:@stdlib/assert/is-almost-equal-float64array}
+*/
+setReadOnly( ns, 'isAlmostEqualFloat64Array', require( '@stdlib/assert/is-almost-equal-float64array' ) );
+
/**
* @name isAlphagram
* @memberof ns
diff --git a/lib/node_modules/@stdlib/blas/base/dgemv/benchmark/c/benchmark.length.c b/lib/node_modules/@stdlib/blas/base/dgemv/benchmark/c/benchmark.length.c
index cc9b9e8fa02d..912eaae082b8 100644
--- a/lib/node_modules/@stdlib/blas/base/dgemv/benchmark/c/benchmark.length.c
+++ b/lib/node_modules/@stdlib/blas/base/dgemv/benchmark/c/benchmark.length.c
@@ -103,11 +103,12 @@ static double benchmark1( int iterations, int N ) {
int i;
int j;
- for ( i = 0, j = 0; i < N; i++, j += 2 ) {
+ for ( i = 0; i < N; i++ ) {
x[ i ] = ( rand_double()*20.0 ) - 10.0;
y[ i ] = ( rand_double()*20.0 ) - 10.0;
- A[ j ] = ( rand_double()*20.0 ) - 10.0;
- A[ j+1 ] = ( rand_double()*20.0 ) - 10.0;
+ for ( j = 0; j < N; j++ ) {
+ A[ (i*N)+j ] = ( rand_double()*20.0 ) - 10.0;
+ }
}
t = tic();
for ( i = 0; i < iterations; i++ ) {
@@ -141,11 +142,12 @@ static double benchmark2( int iterations, int N ) {
int i;
int j;
- for ( i = 0, j = 0; i < N; i++, j += 2 ) {
+ for ( i = 0; i < N; i++ ) {
x[ i ] = ( rand_double()*20.0 ) - 10.0;
y[ i ] = ( rand_double()*20.0 ) - 10.0;
- A[ j ] = ( rand_double()*20.0 ) - 10.0;
- A[ j+1 ] = ( rand_double()*20.0 ) - 10.0;
+ for ( j = 0; j < N; j++ ) {
+ A[ (i*N)+j ] = ( rand_double()*20.0 ) - 10.0;
+ }
}
t = tic();
for ( i = 0; i < iterations; i++ ) {
diff --git a/lib/node_modules/@stdlib/blas/base/dger/benchmark/c/benchmark.length.c b/lib/node_modules/@stdlib/blas/base/dger/benchmark/c/benchmark.length.c
index 7c4df3f89600..754dc8568cab 100644
--- a/lib/node_modules/@stdlib/blas/base/dger/benchmark/c/benchmark.length.c
+++ b/lib/node_modules/@stdlib/blas/base/dger/benchmark/c/benchmark.length.c
@@ -104,11 +104,12 @@ static double benchmark1( int iterations, int N ) {
int i;
int j;
- for ( i = 0, j = 0; i < N; i++, j += 2 ) {
+ for ( i = 0; i < N; i++ ) {
x[ i ] = ( rand_double()*20.0 ) - 10.0;
y[ i ] = ( rand_double()*20.0 ) - 10.0;
- A[ j ] = ( rand_double()*20.0 ) - 10.0;
- A[ j+1 ] = ( rand_double()*20.0 ) - 10.0;
+ for ( j = 0; j < N; j++ ) {
+ A[ (i*N)+j ] = ( rand_double()*20.0 ) - 10.0;
+ }
}
t = tic();
for ( i = 0; i < iterations; i++ ) {
@@ -142,11 +143,12 @@ static double benchmark2( int iterations, int N ) {
int i;
int j;
- for ( i = 0, j = 0; i < N; i++, j += 2 ) {
+ for ( i = 0; i < N; i++ ) {
x[ i ] = ( rand_double()*20.0 ) - 10.0;
y[ i ] = ( rand_double()*20.0 ) - 10.0;
- A[ j ] = ( rand_double()*20.0 ) - 10.0;
- A[ j+1 ] = ( rand_double()*20.0 ) - 10.0;
+ for ( j = 0; j < N; j++ ) {
+ A[ (i*N)+j ] = ( rand_double()*20.0 ) - 10.0;
+ }
}
t = tic();
for ( i = 0; i < iterations; i++ ) {
diff --git a/lib/node_modules/@stdlib/blas/base/sgemv/benchmark/c/benchmark.length.c b/lib/node_modules/@stdlib/blas/base/sgemv/benchmark/c/benchmark.length.c
index 59a3d32b0d0a..4f90a56409e5 100644
--- a/lib/node_modules/@stdlib/blas/base/sgemv/benchmark/c/benchmark.length.c
+++ b/lib/node_modules/@stdlib/blas/base/sgemv/benchmark/c/benchmark.length.c
@@ -103,11 +103,12 @@ static double benchmark1( int iterations, int N ) {
int i;
int j;
- for ( i = 0, j = 0; i < N; i++, j += 2 ) {
+ for ( i = 0; i < N; i++ ) {
x[ i ] = ( rand_float()*20.0f ) - 10.0f;
y[ i ] = ( rand_float()*20.0f ) - 10.0f;
- A[ j ] = ( rand_float()*20.0f ) - 10.0f;
- A[ j+1 ] = ( rand_float()*20.0f ) - 10.0f;
+ for ( j = 0; j < N; j++ ) {
+ A[ (i*N)+j ] = ( rand_float()*20.0f ) - 10.0f;
+ }
}
t = tic();
for ( i = 0; i < iterations; i++ ) {
@@ -141,11 +142,12 @@ static double benchmark2( int iterations, int N ) {
int i;
int j;
- for ( i = 0, j = 0; i < N; i++, j += 2 ) {
+ for ( i = 0; i < N; i++ ) {
x[ i ] = ( rand_float()*20.0f ) - 10.0f;
y[ i ] = ( rand_float()*20.0f ) - 10.0f;
- A[ j ] = ( rand_float()*20.0f ) - 10.0f;
- A[ j+1 ] = ( rand_float()*20.0f ) - 10.0f;
+ for ( j = 0; j < N; j++ ) {
+ A[ (i*N)+j ] = ( rand_float()*20.0f ) - 10.0f;
+ }
}
t = tic();
for ( i = 0; i < iterations; i++ ) {
diff --git a/lib/node_modules/@stdlib/blas/base/sger/benchmark/c/benchmark.length.c b/lib/node_modules/@stdlib/blas/base/sger/benchmark/c/benchmark.length.c
index 2a439460f7d4..c0539ad9f739 100644
--- a/lib/node_modules/@stdlib/blas/base/sger/benchmark/c/benchmark.length.c
+++ b/lib/node_modules/@stdlib/blas/base/sger/benchmark/c/benchmark.length.c
@@ -104,11 +104,12 @@ static double benchmark1( int iterations, int N ) {
int i;
int j;
- for ( i = 0, j = 0; i < N; i++, j += 2 ) {
+ for ( i = 0; i < N; i++ ) {
x[ i ] = ( rand_float()*20.0f ) - 10.0f;
y[ i ] = ( rand_float()*20.0f ) - 10.0f;
- A[ j ] = ( rand_float()*20.0f ) - 10.0f;
- A[ j+1 ] = ( rand_float()*20.0f ) - 10.0f;
+ for ( j = 0; j < N; j++ ) {
+ A[ (i*N)+j ] = ( rand_float()*20.0f ) - 10.0f;
+ }
}
t = tic();
for ( i = 0; i < iterations; i++ ) {
@@ -142,11 +143,12 @@ static double benchmark2( int iterations, int N ) {
int i;
int j;
- for ( i = 0, j = 0; i < N; i++, j += 2 ) {
+ for ( i = 0; i < N; i++ ) {
x[ i ] = ( rand_float()*20.0f ) - 10.0f;
y[ i ] = ( rand_float()*20.0f ) - 10.0f;
- A[ j ] = ( rand_float()*20.0f ) - 10.0f;
- A[ j+1 ] = ( rand_float()*20.0f ) - 10.0f;
+ for ( j = 0; j < N; j++ ) {
+ A[ (i*N)+j ] = ( rand_float()*20.0f ) - 10.0f;
+ }
}
t = tic();
for ( i = 0; i < iterations; i++ ) {
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/README.md b/lib/node_modules/@stdlib/lapack/base/dlacpy/README.md
index 33e43898d079..1a2aa16e425e 100644
--- a/lib/node_modules/@stdlib/lapack/base/dlacpy/README.md
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/README.md
@@ -188,21 +188,66 @@ console.log( ndarray2array( B, shape, strides, 0, order ) );
### Usage
```c
-TODO
+#include "stdlib/lapack/base/dlacpy.h"
```
-#### TODO
+#### c_dlacpy( layout, uplo, M, N, \*A, LDA, \*B, LDB )
-TODO.
+Copies all or part of a matrix `A` to another matrix `B`.
```c
-TODO
+#include "stdlib/lapack/base/shared.h"
+
+const double A[] = { 1.0, 2.0, 3.0, 4.0 };
+double B[] = { 0.0, 0.0, 0.0, 0.0 };
+
+c_dlacpy( LAPACK_ROW_MAJOR, LAPACK_UPPER_TRIANGLE, 2, 2, A, 2, B, 2 );
```
-TODO
+The function accepts the following arguments:
+
+- **order**: `[in] LAPACK_LAYOUT` storage layout.
+- **uplo**: `[in] int` specifies whether to copy the upper or lower triangular/trapezoidal part of a matrix `A`.
+- **M**: `[in] LAPACK_INT` number of rows in `A`.
+- **N**: `[in] LAPACK_INT` number of columns in `A`.
+- **A**: `[in] double*` input matrix.
+- **LDA**: `[in] LAPACK_INT` stride of the first dimension of `A` (a.k.a., leading dimension of the matrix `A`).
+- **B**: `[out] double*` output matrix.
+- **LDB**: `[in] LAPACK_INT` stride of the first dimension of `B` (a.k.a., leading dimension of the matrix `B`).
+
+```c
+LAPACK_INT c_dlacpy( const LAPACK_LAYOUT layout, const int uplo, const LAPACK_INT M, const LAPACK_INT N, const double *A, const LAPACK_INT LDA, double *B, const LAPACK_INT LDB );
+```
+
+#### c_dlacpy_ndarray( uplo, M, N, \*A, sa1, sa2, oa, \*B, sb1, sb2, ob )
+
+Copies all or part of a matrix `A` to another matrix `B` using alternative indexing semantics.
+
+```c
+#include "stdlib/lapack/base/shared.h"
+
+const double A[] = { 1.0, 2.0, 3.0, 4.0 };
+double B[] = { 0.0, 0.0, 0.0, 0.0 };
+
+c_dlacpy_ndarray( LAPACK_UPPER_TRIANGLE, 2, 2, A, 2, 1, 0, B, 2, 1, 0 );
+```
+
+The function accepts the following arguments:
+
+- **uplo**: `[in] int` specifies whether to copy the upper or lower triangular/trapezoidal part of a matrix `A`.
+- **M**: `[in] LAPACK_INT` number of rows in `A`.
+- **N**: `[in] LAPACK_INT` number of columns in `A`.
+- **A**: `[in] double*` input matrix.
+- **sa1**: `[in] LAPACK_INT` stride of the first dimension of `A`.
+- **sa2**: `[in] LAPACK_INT` stride of the second dimension of `A`.
+- **oa**: `[in] LAPACK_INT` starting index for `A`.
+- **B**: `[out] double*` output matrix.
+- **sb1**: `[in] LAPACK_INT` stride of the first dimension of `B`.
+- **sb2**: `[in] LAPACK_INT` stride of the second dimension of `B`.
+- **ob**: `[in] LAPACK_INT` starting index for `B`.
```c
-TODO
+LAPACK_INT c_dlacpy_ndarray( const int uplo, const LAPACK_INT M, const LAPACK_INT N, const double *A, const LAPACK_INT strideA1, const LAPACK_INT strideA2, const LAPACK_INT offsetA, double *B, const LAPACK_INT strideB1, const LAPACK_INT strideB2, const LAPACK_INT offsetB );
```
@@ -224,7 +269,49 @@ TODO
### Examples
```c
-TODO
+#include "stdlib/lapack/base/dlacpy.h"
+#include "stdlib/lapack/base/shared.h"
+#include
+
+int main( void ) {
+ // Define a 3x3 input matrix stored in row-major order:
+ const double A[ 3*3 ] = {
+ 1.0, 2.0, 3.0,
+ 4.0, 5.0, 6.0,
+ 7.0, 8.0, 9.0
+ };
+
+ // Define a 3x3 output matrix:
+ double B[ 3*3 ] = {
+ 0.0, 0.0, 0.0,
+ 0.0, 0.0, 0.0,
+ 0.0, 0.0, 0.0
+ };
+
+ // Specify the number of elements along each dimension of `A`:
+ const int M = 3;
+ const int N = 3;
+
+ // Copy elements from the upper triangle of `A` to `B`:
+ c_dlacpy( LAPACK_ROW_MAJOR, LAPACK_UPPER_TRIANGLE, M, N, A, N, B, N );
+
+ // Print the result:
+ for ( int i = 0; i < M; i++ ) {
+ for ( int j = 0; j < N; j++ ) {
+ printf( "B[ %i, %i ] = %lf\n", i, j, B[ (i*N)+j ] );
+ }
+ }
+
+ // Copy elements from the lower triangle of `A` to `B` using alternative indexing semantics:
+ c_dlacpy_ndarray( LAPACK_LOWER_TRIANGLE, M, N, A, N, 1, 0, B, N, 1, 0 );
+
+ // Print the result:
+ for ( int i = 0; i < M; i++ ) {
+ for ( int j = 0; j < N; j++ ) {
+ printf( "B[ %i, %i ] = %lf\n", i, j, B[ (i*N)+j ] );
+ }
+ }
+}
```
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/benchmark/c/Makefile b/lib/node_modules/@stdlib/lapack/base/dlacpy/benchmark/c/Makefile
new file mode 100644
index 000000000000..bde38c2fd1d2
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/benchmark/c/Makefile
@@ -0,0 +1,146 @@
+#/
+# @license Apache-2.0
+#
+# Copyright (c) 2025 The Stdlib Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#/
+
+# VARIABLES #
+
+ifndef VERBOSE
+ QUIET := @
+else
+ QUIET :=
+endif
+
+# Determine the OS ([1][1], [2][2]).
+#
+# [1]: https://en.wikipedia.org/wiki/Uname#Examples
+# [2]: http://stackoverflow.com/a/27776822/2225624
+OS ?= $(shell uname)
+ifneq (, $(findstring MINGW,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring MSYS,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring CYGWIN,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring Windows_NT,$(OS)))
+ OS := WINNT
+endif
+endif
+endif
+endif
+
+# Define the program used for compiling C source files:
+ifdef C_COMPILER
+ CC := $(C_COMPILER)
+else
+ CC := gcc
+endif
+
+# Define the command-line options when compiling C files:
+CFLAGS ?= \
+ -std=c99 \
+ -O3 \
+ -Wall \
+ -pedantic
+
+# Determine whether to generate position independent code ([1][1], [2][2]).
+#
+# [1]: https://gcc.gnu.org/onlinedocs/gcc/Code-Gen-Options.html#Code-Gen-Options
+# [2]: http://stackoverflow.com/questions/5311515/gcc-fpic-option
+ifeq ($(OS), WINNT)
+ fPIC ?=
+else
+ fPIC ?= -fPIC
+endif
+
+# List of includes (e.g., `-I /foo/bar -I /beep/boop/include`):
+INCLUDE ?=
+
+# List of source files:
+SOURCE_FILES ?=
+
+# List of libraries (e.g., `-lopenblas -lpthread`):
+LIBRARIES ?=
+
+# List of library paths (e.g., `-L /foo/bar -L /beep/boop`):
+LIBPATH ?=
+
+# List of C targets:
+c_targets := benchmark.size.out
+
+
+# RULES #
+
+#/
+# Compiles source files.
+#
+# @param {string} [C_COMPILER] - C compiler (e.g., `gcc`)
+# @param {string} [CFLAGS] - C compiler options
+# @param {(string|void)} [fPIC] - compiler flag determining whether to generate position independent code (e.g., `-fPIC`)
+# @param {string} [INCLUDE] - list of includes (e.g., `-I /foo/bar -I /beep/boop/include`)
+# @param {string} [SOURCE_FILES] - list of source files
+# @param {string} [LIBPATH] - list of library paths (e.g., `-L /foo/bar -L /beep/boop`)
+# @param {string} [LIBRARIES] - list of libraries (e.g., `-lopenblas -lpthread`)
+#
+# @example
+# make
+#
+# @example
+# make all
+#/
+all: $(c_targets)
+
+.PHONY: all
+
+#/
+# Compiles C source files.
+#
+# @private
+# @param {string} CC - C compiler (e.g., `gcc`)
+# @param {string} CFLAGS - C compiler options
+# @param {(string|void)} fPIC - compiler flag determining whether to generate position independent code (e.g., `-fPIC`)
+# @param {string} INCLUDE - list of includes (e.g., `-I /foo/bar`)
+# @param {string} SOURCE_FILES - list of source files
+# @param {string} LIBPATH - list of library paths (e.g., `-L /foo/bar`)
+# @param {string} LIBRARIES - list of libraries (e.g., `-lopenblas`)
+#/
+$(c_targets): %.out: %.c
+ $(QUIET) $(CC) $(CFLAGS) $(fPIC) $(INCLUDE) -o $@ $(SOURCE_FILES) $< $(LIBPATH) -lm $(LIBRARIES)
+
+#/
+# Runs compiled benchmarks.
+#
+# @example
+# make run
+#/
+run: $(c_targets)
+ $(QUIET) ./$<
+
+.PHONY: run
+
+#/
+# Removes generated files.
+#
+# @example
+# make clean
+#/
+clean:
+ $(QUIET) -rm -f *.o *.out
+
+.PHONY: clean
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/benchmark/c/benchmark.size.c b/lib/node_modules/@stdlib/lapack/base/dlacpy/benchmark/c/benchmark.size.c
new file mode 100644
index 000000000000..20b12950e661
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/benchmark/c/benchmark.size.c
@@ -0,0 +1,199 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#include "stdlib/lapack/base/dlacpy.h"
+#include
+#include
+#include
+#include
+#include
+
+#define NAME "dlacpy"
+#define ITERATIONS 10000000
+#define REPEATS 3
+#define MIN 1
+#define MAX 6
+
+/**
+* Prints the TAP version.
+*/
+static void print_version( void ) {
+ printf( "TAP version 13\n" );
+}
+
+/**
+* Prints the TAP summary.
+*
+* @param total total number of tests
+* @param passing total number of passing tests
+*/
+static void print_summary( int total, int passing ) {
+ printf( "#\n" );
+ printf( "1..%d\n", total ); // TAP plan
+ printf( "# total %d\n", total );
+ printf( "# pass %d\n", passing );
+ printf( "#\n" );
+ printf( "# ok\n" );
+}
+
+/**
+* Prints benchmarks results.
+*
+* @param iterations number of iterations
+* @param elapsed elapsed time in seconds
+*/
+static void print_results( int iterations, double elapsed ) {
+ double rate = (double)iterations / elapsed;
+ printf( " ---\n" );
+ printf( " iterations: %d\n", iterations );
+ printf( " elapsed: %0.9f\n", elapsed );
+ printf( " rate: %0.9f\n", rate );
+ printf( " ...\n" );
+}
+
+/**
+* Returns a clock time.
+*
+* @return clock time
+*/
+static double tic( void ) {
+ struct timeval now;
+ gettimeofday( &now, NULL );
+ return (double)now.tv_sec + (double)now.tv_usec/1.0e6;
+}
+
+/**
+* Generates a random number on the interval [0,1).
+*
+* @return random number
+*/
+static double rand_double( void ) {
+ int r = rand();
+ return (double)r / ( (double)RAND_MAX + 1.0 );
+}
+
+/**
+* Runs a benchmark.
+*
+* @param iterations number of iterations
+* @param N array dimension size
+* @return elapsed time in seconds
+*/
+static double benchmark1( int iterations, int N ) {
+ double elapsed;
+ double A[ N*N ];
+ double B[ N*N ];
+ double t;
+ int i;
+ int j;
+
+ for ( i = 0, j = 0; i < N; i++, j += 2 ) {
+ A[ j ] = ( rand_double()*20.0 ) - 10.0;
+ A[ j+1 ] = ( rand_double()*20.0 ) - 10.0;
+ B[ j ] = 0.0;
+ B[ j+1 ] = 0.0;
+ }
+ t = tic();
+ for ( i = 0; i < iterations; i++ ) {
+ // cppcheck-suppress uninitvar
+ c_dlacpy( LAPACK_ROW_MAJOR, -1, N, N, A, N, B, N );
+ if ( B[ i%(N*2) ] != B[ i%(N*2) ] ) {
+ printf( "should not return NaN\n" );
+ break;
+ }
+ }
+ elapsed = tic() - t;
+ if ( B[ i%(N*2) ] != B[ i%(N*2) ] ) {
+ printf( "should not return NaN\n" );
+ }
+ return elapsed;
+}
+
+/**
+* Runs a benchmark.
+*
+* @param iterations number of iterations
+* @param N array dimension size
+* @return elapsed time in seconds
+*/
+static double benchmark2( int iterations, int N ) {
+ double elapsed;
+ double A[ N*N ];
+ double B[ N*N ];
+ double t;
+ int i;
+ int j;
+
+ for ( i = 0, j = 0; i < N; i++, j += 2 ) {
+ A[ j ] = ( rand_double()*20.0 ) - 10.0;
+ A[ j+1 ] = ( rand_double()*20.0 ) - 10.0;
+ B[ j ] = 0.0;
+ B[ j+1 ] = 0.0;
+ }
+ t = tic();
+ for ( i = 0; i < iterations; i++ ) {
+ // cppcheck-suppress uninitvar
+ c_dlacpy_ndarray( -1, N, N, A, N, 1, 0, B, N, 1, 0 );
+ if ( B[ i%(N*2) ] != B[ i%(N*2) ] ) {
+ printf( "should not return NaN\n" );
+ break;
+ }
+ }
+ elapsed = tic() - t;
+ if ( B[ i%(N*2) ] != B[ i%(N*2) ] ) {
+ printf( "should not return NaN\n" );
+ }
+ return elapsed;
+}
+
+/**
+* Main execution sequence.
+*/
+int main( void ) {
+ double elapsed;
+ int count;
+ int iter;
+ int N;
+ int i;
+ int j;
+
+ // Use the current time to seed the random number generator:
+ srand( time( NULL ) );
+
+ print_version();
+ count = 0;
+ for ( i = MIN; i <= MAX; i++ ) {
+ N = floor( pow( pow( 10, i ), 1.0/2.0 ) );
+ iter = ITERATIONS / pow( 10, i-1 );
+ for ( j = 0; j < REPEATS; j++ ) {
+ count += 1;
+ printf( "# c::%s:size=%d\n", NAME, N*N );
+ elapsed = benchmark1( iter, N );
+ print_results( iter, elapsed );
+ printf( "ok %d benchmark finished\n", count );
+ }
+ for ( j = 0; j < REPEATS; j++ ) {
+ count += 1;
+ printf( "# c::%s:ndarray:size=%d\n", NAME, N*N );
+ elapsed = benchmark2( iter, N );
+ print_results( iter, elapsed );
+ printf( "ok %d benchmark finished\n", count );
+ }
+ }
+ print_summary( count, count );
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/binding.gyp b/lib/node_modules/@stdlib/lapack/base/dlacpy/binding.gyp
new file mode 100644
index 000000000000..08de71a2020e
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/binding.gyp
@@ -0,0 +1,265 @@
+# @license Apache-2.0
+#
+# Copyright (c) 2025 The Stdlib Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# A `.gyp` file for building a Node.js native add-on.
+#
+# [1]: https://gyp.gsrc.io/docs/InputFormatReference.md
+# [2]: https://gyp.gsrc.io/docs/UserDocumentation.md
+{
+ # List of files to include in this file:
+ 'includes': [
+ './include.gypi',
+ ],
+
+ # Define variables to be used throughout the configuration for all targets:
+ 'variables': {
+ # Target name should match the add-on export name:
+ 'addon_target_name%': 'addon',
+
+ # Fortran compiler (to override -Dfortran_compiler=):
+ 'fortran_compiler%': 'gfortran',
+
+ # Fortran compiler flags:
+ 'fflags': [
+ # Specify the Fortran standard to which a program is expected to conform:
+ '-std=f95',
+
+ # Indicate that the layout is free-form source code:
+ '-ffree-form',
+
+ # Aggressive optimization:
+ '-O3',
+
+ # Enable commonly used warning options:
+ '-Wall',
+
+ # Warn if source code contains problematic language features:
+ '-Wextra',
+
+ # Warn if a procedure is called without an explicit interface:
+ '-Wimplicit-interface',
+
+ # Do not transform names of entities specified in Fortran source files by appending underscores (i.e., don't mangle names, thus allowing easier usage in C wrappers):
+ '-fno-underscoring',
+
+ # Warn if source code contains Fortran 95 extensions and C-language constructs:
+ '-pedantic',
+
+ # Compile but do not link (output is an object file):
+ '-c',
+ ],
+
+ # Set variables based on the host OS:
+ 'conditions': [
+ [
+ 'OS=="win"',
+ {
+ # Define the object file suffix:
+ 'obj': 'obj',
+ },
+ {
+ # Define the object file suffix:
+ 'obj': 'o',
+ }
+ ], # end condition (OS=="win")
+ ], # end conditions
+ }, # end variables
+
+ # Define compile targets:
+ 'targets': [
+
+ # Target to generate an add-on:
+ {
+ # The target name should match the add-on export name:
+ 'target_name': '<(addon_target_name)',
+
+ # Define dependencies:
+ 'dependencies': [],
+
+ # Define directories which contain relevant include headers:
+ 'include_dirs': [
+ # Local include directory:
+ '<@(include_dirs)',
+ ],
+
+ # List of source files:
+ 'sources': [
+ '<@(src_files)',
+ ],
+
+ # Settings which should be applied when a target's object files are used as linker input:
+ 'link_settings': {
+ # Define libraries:
+ 'libraries': [
+ '<@(libraries)',
+ ],
+
+ # Define library directories:
+ 'library_dirs': [
+ '<@(library_dirs)',
+ ],
+ },
+
+ # C/C++ compiler flags:
+ 'cflags': [
+ # Enable commonly used warning options:
+ '-Wall',
+
+ # Aggressive optimization:
+ '-O3',
+ ],
+
+ # C specific compiler flags:
+ 'cflags_c': [
+ # Specify the C standard to which a program is expected to conform:
+ '-std=c99',
+ ],
+
+ # C++ specific compiler flags:
+ 'cflags_cpp': [
+ # Specify the C++ standard to which a program is expected to conform:
+ '-std=c++11',
+ ],
+
+ # Linker flags:
+ 'ldflags': [],
+
+ # Apply conditions based on the host OS:
+ 'conditions': [
+ [
+ 'OS=="mac"',
+ {
+ # Linker flags:
+ 'ldflags': [
+ '-undefined dynamic_lookup',
+ '-Wl,-no-pie',
+ '-Wl,-search_paths_first',
+ ],
+ },
+ ], # end condition (OS=="mac")
+ [
+ 'OS!="win"',
+ {
+ # C/C++ flags:
+ 'cflags': [
+ # Generate platform-independent code:
+ '-fPIC',
+ ],
+ },
+ ], # end condition (OS!="win")
+ ], # end conditions
+
+ # Define custom build actions for particular inputs:
+ 'rules': [
+ {
+ # Define a rule for processing Fortran files:
+ 'extension': 'f',
+
+ # Define the pathnames to be used as inputs when performing processing:
+ 'inputs': [
+ # Full path of the current input:
+ '<(RULE_INPUT_PATH)'
+ ],
+
+ # Define the outputs produced during processing:
+ 'outputs': [
+ # Store an output object file in a directory for placing intermediate results (only accessible within a single target):
+ '<(INTERMEDIATE_DIR)/<(RULE_INPUT_ROOT).<(obj)'
+ ],
+
+ # Define the rule for compiling Fortran based on the host OS:
+ 'conditions': [
+ [
+ 'OS=="win"',
+
+ # Rule to compile Fortran on Windows:
+ {
+ 'rule_name': 'compile_fortran_windows',
+ 'message': 'Compiling Fortran file <(RULE_INPUT_PATH) on Windows...',
+
+ 'process_outputs_as_sources': 0,
+
+ # Define the command-line invocation:
+ 'action': [
+ '<(fortran_compiler)',
+ '<@(fflags)',
+ '<@(_inputs)',
+ '-o',
+ '<@(_outputs)',
+ ],
+ },
+
+ # Rule to compile Fortran on non-Windows:
+ {
+ 'rule_name': 'compile_fortran_linux',
+ 'message': 'Compiling Fortran file <(RULE_INPUT_PATH) on Linux...',
+
+ 'process_outputs_as_sources': 1,
+
+ # Define the command-line invocation:
+ 'action': [
+ '<(fortran_compiler)',
+ '<@(fflags)',
+ '-fPIC', # generate platform-independent code
+ '<@(_inputs)',
+ '-o',
+ '<@(_outputs)',
+ ],
+ }
+ ], # end condition (OS=="win")
+ ], # end conditions
+ }, # end rule (extension=="f")
+ ], # end rules
+ }, # end target <(addon_target_name)
+
+ # Target to copy a generated add-on to a standard location:
+ {
+ 'target_name': 'copy_addon',
+
+ # Declare that the output of this target is not linked:
+ 'type': 'none',
+
+ # Define dependencies:
+ 'dependencies': [
+ # Require that the add-on be generated before building this target:
+ '<(addon_target_name)',
+ ],
+
+ # Define a list of actions:
+ 'actions': [
+ {
+ 'action_name': 'copy_addon',
+ 'message': 'Copying addon...',
+
+ # Explicitly list the inputs in the command-line invocation below:
+ 'inputs': [],
+
+ # Declare the expected outputs:
+ 'outputs': [
+ '<(addon_output_dir)/<(addon_target_name).node',
+ ],
+
+ # Define the command-line invocation:
+ 'action': [
+ 'cp',
+ '<(PRODUCT_DIR)/<(addon_target_name).node',
+ '<(addon_output_dir)/<(addon_target_name).node',
+ ],
+ },
+ ], # end actions
+ }, # end target copy_addon
+ ], # end targets
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/examples/c/Makefile b/lib/node_modules/@stdlib/lapack/base/dlacpy/examples/c/Makefile
new file mode 100644
index 000000000000..25ced822f96a
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/examples/c/Makefile
@@ -0,0 +1,146 @@
+#/
+# @license Apache-2.0
+#
+# Copyright (c) 2025 The Stdlib Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#/
+
+# VARIABLES #
+
+ifndef VERBOSE
+ QUIET := @
+else
+ QUIET :=
+endif
+
+# Determine the OS ([1][1], [2][2]).
+#
+# [1]: https://en.wikipedia.org/wiki/Uname#Examples
+# [2]: http://stackoverflow.com/a/27776822/2225624
+OS ?= $(shell uname)
+ifneq (, $(findstring MINGW,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring MSYS,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring CYGWIN,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring Windows_NT,$(OS)))
+ OS := WINNT
+endif
+endif
+endif
+endif
+
+# Define the program used for compiling C source files:
+ifdef C_COMPILER
+ CC := $(C_COMPILER)
+else
+ CC := gcc
+endif
+
+# Define the command-line options when compiling C files:
+CFLAGS ?= \
+ -std=c99 \
+ -O3 \
+ -Wall \
+ -pedantic
+
+# Determine whether to generate position independent code ([1][1], [2][2]).
+#
+# [1]: https://gcc.gnu.org/onlinedocs/gcc/Code-Gen-Options.html#Code-Gen-Options
+# [2]: http://stackoverflow.com/questions/5311515/gcc-fpic-option
+ifeq ($(OS), WINNT)
+ fPIC ?=
+else
+ fPIC ?= -fPIC
+endif
+
+# List of includes (e.g., `-I /foo/bar -I /beep/boop/include`):
+INCLUDE ?=
+
+# List of source files:
+SOURCE_FILES ?=
+
+# List of libraries (e.g., `-lopenblas -lpthread`):
+LIBRARIES ?=
+
+# List of library paths (e.g., `-L /foo/bar -L /beep/boop`):
+LIBPATH ?=
+
+# List of C targets:
+c_targets := example.out
+
+
+# RULES #
+
+#/
+# Compiles source files.
+#
+# @param {string} [C_COMPILER] - C compiler (e.g., `gcc`)
+# @param {string} [CFLAGS] - C compiler options
+# @param {(string|void)} [fPIC] - compiler flag determining whether to generate position independent code (e.g., `-fPIC`)
+# @param {string} [INCLUDE] - list of includes (e.g., `-I /foo/bar -I /beep/boop/include`)
+# @param {string} [SOURCE_FILES] - list of source files
+# @param {string} [LIBPATH] - list of library paths (e.g., `-L /foo/bar -L /beep/boop`)
+# @param {string} [LIBRARIES] - list of libraries (e.g., `-lopenblas -lpthread`)
+#
+# @example
+# make
+#
+# @example
+# make all
+#/
+all: $(c_targets)
+
+.PHONY: all
+
+#/
+# Compiles C source files.
+#
+# @private
+# @param {string} CC - C compiler (e.g., `gcc`)
+# @param {string} CFLAGS - C compiler options
+# @param {(string|void)} fPIC - compiler flag determining whether to generate position independent code (e.g., `-fPIC`)
+# @param {string} INCLUDE - list of includes (e.g., `-I /foo/bar`)
+# @param {string} SOURCE_FILES - list of source files
+# @param {string} LIBPATH - list of library paths (e.g., `-L /foo/bar`)
+# @param {string} LIBRARIES - list of libraries (e.g., `-lopenblas`)
+#/
+$(c_targets): %.out: %.c
+ $(QUIET) $(CC) $(CFLAGS) $(fPIC) $(INCLUDE) -o $@ $(SOURCE_FILES) $< $(LIBPATH) -lm $(LIBRARIES)
+
+#/
+# Runs compiled examples.
+#
+# @example
+# make run
+#/
+run: $(c_targets)
+ $(QUIET) ./$<
+
+.PHONY: run
+
+#/
+# Removes generated files.
+#
+# @example
+# make clean
+#/
+clean:
+ $(QUIET) -rm -f *.o *.out
+
+.PHONY: clean
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/examples/c/example.c b/lib/node_modules/@stdlib/lapack/base/dlacpy/examples/c/example.c
new file mode 100644
index 000000000000..5a88c21974fb
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/examples/c/example.c
@@ -0,0 +1,61 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#include "stdlib/lapack/base/dlacpy.h"
+#include "stdlib/lapack/base/shared.h"
+#include
+
+int main( void ) {
+ // Define a 3x3 input matrix stored in row-major order:
+ const double A[ 3*3 ] = {
+ 1.0, 2.0, 3.0,
+ 4.0, 5.0, 6.0,
+ 7.0, 8.0, 9.0
+ };
+
+ // Define a 3x3 output matrix:
+ double B[ 3*3 ] = {
+ 0.0, 0.0, 0.0,
+ 0.0, 0.0, 0.0,
+ 0.0, 0.0, 0.0
+ };
+
+ // Specify the number of elements along each dimension of `A`:
+ const int M = 3;
+ const int N = 3;
+
+ // Copy elements from the upper triangle of `A` to `B`:
+ c_dlacpy( LAPACK_ROW_MAJOR, LAPACK_UPPER_TRIANGLE, M, N, A, N, B, N );
+
+ // Print the result:
+ for ( int i = 0; i < M; i++ ) {
+ for ( int j = 0; j < N; j++ ) {
+ printf( "B[ %i, %i ] = %lf\n", i, j, B[ (i*N)+j ] );
+ }
+ }
+
+ // Copy elements from the lower triangle of `A` to `B` using alternative indexing semantics:
+ c_dlacpy_ndarray( LAPACK_LOWER_TRIANGLE, M, N, A, N, 1, 0, B, N, 1, 0 );
+
+ // Print the result:
+ for ( int i = 0; i < M; i++ ) {
+ for ( int j = 0; j < N; j++ ) {
+ printf( "B[ %i, %i ] = %lf\n", i, j, B[ (i*N)+j ] );
+ }
+ }
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/include.gypi b/lib/node_modules/@stdlib/lapack/base/dlacpy/include.gypi
new file mode 100644
index 000000000000..4217944b5d20
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/include.gypi
@@ -0,0 +1,70 @@
+# @license Apache-2.0
+#
+# Copyright (c) 2025 The Stdlib Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# A GYP include file for building a Node.js native add-on.
+#
+# Note that nesting variables is required due to how GYP processes a configuration. Any variables defined within a nested 'variables' section is defined in the outer scope. Thus, conditions in the outer variable scope are free to use these variables without running into "variable undefined" errors.
+#
+# Main documentation:
+#
+# [1]: https://gyp.gsrc.io/docs/InputFormatReference.md
+# [2]: https://gyp.gsrc.io/docs/UserDocumentation.md
+#
+# Variable nesting hacks:
+#
+# [3]: https://chromium.googlesource.com/external/skia/gyp/+/master/common_variables.gypi
+# [4]: https://src.chromium.org/viewvc/chrome/trunk/src/build/common.gypi?revision=127004
+{
+ # Define variables to be used throughout the configuration for all targets:
+ 'variables': {
+ 'variables': {
+ # Host BLAS library (to override -Dblas=):
+ 'blas%': '',
+
+ # Path to BLAS library (to override -Dblas_dir=):
+ 'blas_dir%': '',
+ }, # end variables
+
+ # Source directory:
+ 'src_dir': './src',
+
+ # Include directories:
+ 'include_dirs': [
+ '<@(blas_dir)',
+ ' [ 1.0, 2.0, 3.0, 4.0 ]
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+*
+* var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
+* var B = new Float64Array( 4 );
+*
+* dlacpy( 'row-major', 'upper', 2, 2, A, 2, B, 2 );
+* // B => [ 1.0, 2.0, 0.0, 4.0 ]
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+*
+* var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
+* var B = new Float64Array( 4 );
+*
+* dlacpy( 'row-major', 'lower', 2, 2, A, 2, B, 2 );
+* // B => [ 1.0, 0.0, 3.0, 4.0 ]
+*/
+function dlacpy( order, uplo, M, N, A, LDA, B, LDB ) {
+ if ( !isLayout( order ) ) {
+ throw new TypeError( format( 'invalid argument. First argument must be a valid order. Value: `%s`.', order ) );
+ }
+ if ( isRowMajor( order ) ) {
+ if ( LDA < N ) {
+ throw new RangeError( format( 'invalid argument. Sixth argument must be greater than or equal to %d. Value: `%d`.', N, LDA ) );
+ }
+ if ( LDB < N ) {
+ throw new RangeError( format( 'invalid argument. Eighth argument must be greater than or equal to %d. Value: `%d`.', N, LDB ) );
+ }
+ }
+ addon( resolveOrder( order ), resolveTriangle( uplo ) || -1, M, N, A, LDA, B, LDB ); // eslint-disable-line max-len
+ return B;
+}
+
+
+// EXPORTS //
+
+module.exports = dlacpy;
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/lib/native.js b/lib/node_modules/@stdlib/lapack/base/dlacpy/lib/native.js
new file mode 100644
index 000000000000..e5847913315c
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/lib/native.js
@@ -0,0 +1,35 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' );
+var dlacpy = require( './dlacpy.native.js' );
+var ndarray = require( './ndarray.native.js' );
+
+
+// MAIN //
+
+setReadOnly( dlacpy, 'ndarray', ndarray );
+
+
+// EXPORTS //
+
+module.exports = dlacpy;
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/lib/ndarray.native.js b/lib/node_modules/@stdlib/lapack/base/dlacpy/lib/ndarray.native.js
new file mode 100644
index 000000000000..eb4d1e4163f9
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/lib/ndarray.native.js
@@ -0,0 +1,80 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2024 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var resolveTriangle = require( '@stdlib/blas/base/matrix-triangle-resolve-enum' );
+var addon = require( './../src/addon.node' );
+
+
+// MAIN //
+
+/**
+* Copies all or part of a matrix `A` to another matrix `B` using alternative indexing semantics.
+*
+* @param {string} uplo - specifies whether to copy the upper or lower triangular/trapezoidal part of matrix `A`
+* @param {NonNegativeInteger} M - number of rows in matrix `A`
+* @param {NonNegativeInteger} N - number of columns in matrix `A`
+* @param {Float64Array} A - input matrix
+* @param {integer} strideA1 - stride of the first dimension of `A`
+* @param {integer} strideA2 - stride of the second dimension of `A`
+* @param {NonNegativeInteger} offsetA - starting index for `A`
+* @param {Float64Array} B - output matrix
+* @param {integer} strideB1 - stride of the first dimension of `B`
+* @param {integer} strideB2 - stride of the second dimension of `B`
+* @param {NonNegativeInteger} offsetB - starting index for `B`
+* @returns {Float64Array} `B`
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+*
+* var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
+* var B = new Float64Array( 6 );
+*
+* dlacpy( 'all', 2, 2, A, 2, 1, 1, B, 2, 1, 2 );
+* // B => [ 0.0, 0.0, 2.0, 3.0, 4.0, 5.0 ]
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+*
+* var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
+* var B = new Float64Array( 6 );
+*
+* dlacpy( 'upper', 2, 2, A, 2, 1, 1, B, 2, 1, 2 );
+* // B => [ 0.0, 0.0, 2.0, 3.0, 0.0, 5.0 ]
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+*
+* var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
+* var B = new Float64Array( 6 );
+*
+* dlacpy( 'lower', 2, 2, A, 2, 1, 1, B, 2, 1, 2 );
+* // B => [ 0.0, 0.0, 2.0, 0.0, 4.0, 5.0 ]
+*/
+function dlacpy( uplo, M, N, A, strideA1, strideA2, offsetA, B, strideB1, strideB2, offsetB ) { // eslint-disable-line max-len, max-params
+ addon.ndarray( resolveTriangle( uplo ) || -1, M, N, A, strideA1, strideA2, offsetA, B, strideB1, strideB2, offsetB ); // eslint-disable-line max-len
+ return B;
+}
+
+
+// EXPORTS //
+
+module.exports = dlacpy;
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/manifest.json b/lib/node_modules/@stdlib/lapack/base/dlacpy/manifest.json
new file mode 100644
index 000000000000..d4c04aa32df8
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/manifest.json
@@ -0,0 +1,250 @@
+{
+ "options": {
+ "task": "build",
+ "os": "linux",
+ "blas": "",
+ "wasm": false
+ },
+ "fields": [
+ {
+ "field": "src",
+ "resolve": true,
+ "relative": true
+ },
+ {
+ "field": "include",
+ "resolve": true,
+ "relative": true
+ },
+ {
+ "field": "libraries",
+ "resolve": false,
+ "relative": false
+ },
+ {
+ "field": "libpath",
+ "resolve": true,
+ "relative": false
+ }
+ ],
+ "confs": [
+ {
+ "task": "build",
+ "os": "linux",
+ "blas": "",
+ "wasm": false,
+ "src": [
+ "./src/dlacpy.c",
+ "./src/dlacpy_ndarray.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/lapack/base/shared",
+ "@stdlib/lapack/base/xerbla",
+ "@stdlib/ndarray/base/assert/is-row-major",
+ "@stdlib/napi/export",
+ "@stdlib/napi/argv",
+ "@stdlib/napi/argv-int64",
+ "@stdlib/napi/argv-int32",
+ "@stdlib/napi/argv-strided-float64array2d"
+ ]
+ },
+ {
+ "task": "benchmark",
+ "os": "linux",
+ "blas": "",
+ "wasm": false,
+ "src": [
+ "./src/dlacpy.c",
+ "./src/dlacpy_ndarray.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/lapack/base/shared",
+ "@stdlib/lapack/base/xerbla",
+ "@stdlib/ndarray/base/assert/is-row-major"
+ ]
+ },
+ {
+ "task": "examples",
+ "os": "linux",
+ "blas": "",
+ "wasm": false,
+ "src": [
+ "./src/dlacpy.c",
+ "./src/dlacpy_ndarray.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/lapack/base/shared",
+ "@stdlib/lapack/base/xerbla",
+ "@stdlib/ndarray/base/assert/is-row-major"
+ ]
+ },
+
+ {
+ "task": "build",
+ "os": "mac",
+ "blas": "",
+ "wasm": false,
+ "src": [
+ "./src/dlacpy.c",
+ "./src/dlacpy_ndarray.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/lapack/base/shared",
+ "@stdlib/lapack/base/xerbla",
+ "@stdlib/ndarray/base/assert/is-row-major",
+ "@stdlib/napi/export",
+ "@stdlib/napi/argv",
+ "@stdlib/napi/argv-int64",
+ "@stdlib/napi/argv-int32",
+ "@stdlib/napi/argv-strided-float64array2d"
+ ]
+ },
+ {
+ "task": "benchmark",
+ "os": "mac",
+ "blas": "",
+ "wasm": false,
+ "src": [
+ "./src/dlacpy.c",
+ "./src/dlacpy_ndarray.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/lapack/base/shared",
+ "@stdlib/lapack/base/xerbla",
+ "@stdlib/ndarray/base/assert/is-row-major"
+ ]
+ },
+ {
+ "task": "examples",
+ "os": "mac",
+ "blas": "",
+ "wasm": false,
+ "src": [
+ "./src/dlacpy.c",
+ "./src/dlacpy_ndarray.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/lapack/base/shared",
+ "@stdlib/lapack/base/xerbla",
+ "@stdlib/ndarray/base/assert/is-row-major"
+ ]
+ },
+
+ {
+ "task": "build",
+ "os": "win",
+ "blas": "",
+ "wasm": false,
+ "src": [
+ "./src/dlacpy.c",
+ "./src/dlacpy_ndarray.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/lapack/base/shared",
+ "@stdlib/lapack/base/xerbla",
+ "@stdlib/ndarray/base/assert/is-row-major",
+ "@stdlib/napi/export",
+ "@stdlib/napi/argv",
+ "@stdlib/napi/argv-int64",
+ "@stdlib/napi/argv-int32",
+ "@stdlib/napi/argv-strided-float64array2d"
+ ]
+ },
+ {
+ "task": "benchmark",
+ "os": "win",
+ "blas": "",
+ "wasm": false,
+ "src": [
+ "./src/dlacpy.c",
+ "./src/dlacpy_ndarray.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/lapack/base/shared",
+ "@stdlib/lapack/base/xerbla",
+ "@stdlib/ndarray/base/assert/is-row-major"
+ ]
+ },
+ {
+ "task": "examples",
+ "os": "win",
+ "blas": "",
+ "wasm": false,
+ "src": [
+ "./src/dlacpy.c",
+ "./src/dlacpy_ndarray.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/lapack/base/shared",
+ "@stdlib/lapack/base/xerbla",
+ "@stdlib/ndarray/base/assert/is-row-major"
+ ]
+ },
+
+ {
+ "task": "build",
+ "os": "",
+ "blas": "",
+ "wasm": true,
+ "src": [
+ "./src/dlacpy.c",
+ "./src/dlacpy_ndarray.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/lapack/base/shared",
+ "@stdlib/lapack/base/xerbla",
+ "@stdlib/ndarray/base/assert/is-row-major"
+ ]
+ }
+ ]
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/package.json b/lib/node_modules/@stdlib/lapack/base/dlacpy/package.json
index 7eed92e7512e..ac5f6ebc2f2d 100644
--- a/lib/node_modules/@stdlib/lapack/base/dlacpy/package.json
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/package.json
@@ -14,11 +14,15 @@
}
],
"main": "./lib",
+ "browser": "./lib/main.js",
+ "gypfile": true,
"directories": {
"benchmark": "./benchmark",
"doc": "./docs",
"example": "./examples",
+ "include": "./include",
"lib": "./lib",
+ "src": "./src",
"test": "./test"
},
"types": "./docs/types",
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/src/Makefile b/lib/node_modules/@stdlib/lapack/base/dlacpy/src/Makefile
new file mode 100644
index 000000000000..7733b6180cb4
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/src/Makefile
@@ -0,0 +1,70 @@
+#/
+# @license Apache-2.0
+#
+# Copyright (c) 2025 The Stdlib Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#/
+
+# VARIABLES #
+
+ifndef VERBOSE
+ QUIET := @
+else
+ QUIET :=
+endif
+
+# Determine the OS ([1][1], [2][2]).
+#
+# [1]: https://en.wikipedia.org/wiki/Uname#Examples
+# [2]: http://stackoverflow.com/a/27776822/2225624
+OS ?= $(shell uname)
+ifneq (, $(findstring MINGW,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring MSYS,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring CYGWIN,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring Windows_NT,$(OS)))
+ OS := WINNT
+endif
+endif
+endif
+endif
+
+
+# RULES #
+
+#/
+# Removes generated files for building an add-on.
+#
+# @example
+# make clean-addon
+#/
+clean-addon:
+ $(QUIET) -rm -f *.o *.node
+
+.PHONY: clean-addon
+
+#/
+# Removes generated files.
+#
+# @example
+# make clean
+#/
+clean: clean-addon
+
+.PHONY: clean
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/src/addon.c b/lib/node_modules/@stdlib/lapack/base/dlacpy/src/addon.c
new file mode 100644
index 000000000000..49a0e4f4659c
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/src/addon.c
@@ -0,0 +1,101 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#include "stdlib/lapack/base/dlacpy.h"
+#include "stdlib/lapack/base/shared.h"
+#include "stdlib/napi/export.h"
+#include "stdlib/napi/argv.h"
+#include "stdlib/napi/argv_int64.h"
+#include "stdlib/napi/argv_int32.h"
+#include "stdlib/napi/argv_strided_float64array2d.h"
+#include
+
+/**
+* Receives JavaScript callback invocation data.
+*
+* @param env environment under which the function is invoked
+* @param info callback data
+* @return Node-API value
+*/
+static napi_value addon( napi_env env, napi_callback_info info ) {
+ CBLAS_INT sa1;
+ CBLAS_INT sa2;
+ CBLAS_INT sb1;
+ CBLAS_INT sb2;
+
+ STDLIB_NAPI_ARGV( env, info, argv, argc, 8 );
+
+ STDLIB_NAPI_ARGV_INT32( env, layout, argv, 0 );
+ STDLIB_NAPI_ARGV_INT32( env, uplo, argv, 1 );
+
+ STDLIB_NAPI_ARGV_INT64( env, M, argv, 2 );
+ STDLIB_NAPI_ARGV_INT64( env, N, argv, 3 );
+ STDLIB_NAPI_ARGV_INT64( env, LDA, argv, 5 );
+ STDLIB_NAPI_ARGV_INT64( env, LDB, argv, 7 );
+
+ if ( layout == LAPACK_COL_MAJOR ) {
+ sa1 = 1;
+ sa2 = LDA;
+ sb1 = 1;
+ sb2 = LDB;
+ } else { // layout == LAPACK_ROW_MAJOR
+ sa1 = LDA;
+ sa2 = 1;
+ sb1 = LDB;
+ sb2 = 1;
+ }
+ STDLIB_NAPI_ARGV_STRIDED_FLOAT64ARRAY2D( env, A, M, N, sa1, sa2, argv, 4 );
+ STDLIB_NAPI_ARGV_STRIDED_FLOAT64ARRAY2D( env, B, M, N, sb1, sb2, argv, 6 );
+
+ API_SUFFIX(c_dlacpy)( layout, uplo, M, N, A, LDA, B, LDB );
+
+ return NULL;
+}
+
+/**
+* Receives JavaScript callback invocation data.
+*
+* @param env environment under which the function is invoked
+* @param info callback data
+* @return Node-API value
+*/
+static napi_value addon_method( napi_env env, napi_callback_info info ) {
+ STDLIB_NAPI_ARGV( env, info, argv, argc, 11 );
+
+ STDLIB_NAPI_ARGV_INT32( env, uplo, argv, 0 );
+
+ STDLIB_NAPI_ARGV_INT64( env, M, argv, 1 );
+ STDLIB_NAPI_ARGV_INT64( env, N, argv, 2 );
+
+ STDLIB_NAPI_ARGV_INT64( env, strideA1, argv, 4 );
+ STDLIB_NAPI_ARGV_INT64( env, strideA2, argv, 5 );
+ STDLIB_NAPI_ARGV_INT64( env, offsetA, argv, 6 );
+
+ STDLIB_NAPI_ARGV_INT64( env, strideB1, argv, 8 );
+ STDLIB_NAPI_ARGV_INT64( env, strideB2, argv, 9 );
+ STDLIB_NAPI_ARGV_INT64( env, offsetB, argv, 10 );
+
+ STDLIB_NAPI_ARGV_STRIDED_FLOAT64ARRAY2D( env, A, M, N, strideA1, strideA2, argv, 3 );
+ STDLIB_NAPI_ARGV_STRIDED_FLOAT64ARRAY2D( env, B, M, N, strideB1, strideB2, argv, 7 );
+
+ API_SUFFIX(c_dlacpy_ndarray)( uplo, M, N, A, strideA1, strideA2, offsetA, B, strideB1, strideB2, offsetB );
+
+ return NULL;
+}
+
+STDLIB_NAPI_MODULE_EXPORT_FCN_WITH_METHOD( addon, "ndarray", addon_method )
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/src/dlacpy.c b/lib/node_modules/@stdlib/lapack/base/dlacpy/src/dlacpy.c
new file mode 100644
index 000000000000..2ad01cec98de
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/src/dlacpy.c
@@ -0,0 +1,59 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#include "stdlib/lapack/base/dlacpy.h"
+#include "stdlib/lapack/base/shared.h"
+#include "stdlib/lapack/base/xerbla.h"
+
+/**
+* Copies all or part of a matrix `A` to another matrix `B`.
+*
+* @param layout storage layout
+* @param uplo specifies whether to copy the upper or lower triangular/trapezoidal part of a matrix `A`
+* @param M number of rows in the matrix `A`
+* @param N number of columns in the matrix `A`
+* @param A input matrix
+* @param LDA stride of the first dimension of `A` (a.k.a., leading dimension of the matrix `A`)
+* @param B output matrix
+* @param LDB stride of the first dimension of `B` (a.k.a., leading dimension of the matrix `B`)
+* @return status code
+*/
+LAPACK_INT API_SUFFIX(c_dlacpy)( const LAPACK_LAYOUT layout, const int uplo, const LAPACK_INT M, const LAPACK_INT N, const double *A, const LAPACK_INT LDA, double *B, const LAPACK_INT LDB ) {
+ LAPACK_INT sa1;
+ LAPACK_INT sa2;
+ LAPACK_INT sb1;
+ LAPACK_INT sb2;
+
+ // Perform input argument validation...
+ if ( layout != LAPACK_ROW_MAJOR && layout != LAPACK_COL_MAJOR ) {
+ lapack_xerbla( "c_dlacpy", -1 );
+ return -1;
+ }
+ if ( layout == LAPACK_COL_MAJOR ) {
+ sa1 = 1;
+ sa2 = LDA;
+ sb1 = 1;
+ sb2 = LDB;
+ } else { // layout == CblasRowMajor
+ sa1 = LDA;
+ sa2 = 1;
+ sb1 = LDB;
+ sb2 = 1;
+ }
+ return API_SUFFIX(c_dlacpy_ndarray)( uplo, M, N, A, sa1, sa2, 0, B, sb1, sb2, 0 );
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/src/dlacpy_ndarray.c b/lib/node_modules/@stdlib/lapack/base/dlacpy/src/dlacpy_ndarray.c
new file mode 100644
index 000000000000..8bf2b40377f5
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/src/dlacpy_ndarray.c
@@ -0,0 +1,211 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#include "stdlib/lapack/base/dlacpy.h"
+#include "stdlib/lapack/base/shared.h"
+#include "stdlib/ndarray/base/assert/is_row_major.h"
+#include
+
+// Define a macro for computing the minimum value:
+#define MIN(X, Y) (((X) < (Y)) ? (X) : (Y))
+
+/**
+* Copies all of a matrix `A` to another matrix `B`.
+*
+* @param M number of rows in the matrix `A`
+* @param N number of columns in the matrix `A`
+* @param A input matrix
+* @param strideA1 stride of the first dimension of `A`
+* @param strideA2 stride of the second dimension of `A`
+* @param offsetA starting index for `A`
+* @param B output matrix
+* @param strideB1 stride of the first dimension of `B`
+* @param strideB2 stride of the second dimension of `B`
+* @param offsetB starting index for `B`
+* @return status code
+*/
+static LAPACK_INT stdlib_lapack_base_copy_all( const LAPACK_INT M, const LAPACK_INT N, const double *A, const LAPACK_INT strideA1, const LAPACK_INT strideA2, const LAPACK_INT offsetA, double *B, const LAPACK_INT strideB1, const LAPACK_INT strideB2, const LAPACK_INT offsetB ) {
+ int64_t sa[ 2 ];
+ LAPACK_INT da0;
+ LAPACK_INT da1;
+ LAPACK_INT db0;
+ LAPACK_INT db1;
+ LAPACK_INT S0;
+ LAPACK_INT S1;
+ LAPACK_INT ia;
+ LAPACK_INT ib;
+ LAPACK_INT i0;
+ LAPACK_INT i1;
+
+ // Extract loop variables for purposes of loop interchange: dimensions and loop offset (pointer) increments...
+ sa[ 0 ] = strideA1;
+ sa[ 1 ] = strideA2;
+ if ( stdlib_ndarray_is_row_major( 2, sa ) ) {
+ // For row-major matrices, the last dimension has the fastest changing index...
+ S0 = N;
+ S1 = M;
+ da0 = strideA2; // offset increment for innermost loop
+ da1 = strideA1 - ( S0*strideA2 ); // offset increment for outermost loop
+ db0 = strideB2;
+ db1 = strideB1 - ( S0*strideB2 );
+ } else { // column-major
+ // For column-major matrices, the first dimension has the fastest changing index...
+ S0 = M;
+ S1 = N;
+ da0 = strideA1; // offset increment for innermost loop
+ da1 = strideA2 - ( S0*strideA1 ); // offset increment for outermost loop
+ db0 = strideB1;
+ db1 = strideB2 - ( S0*strideB1 );
+ }
+
+ // Set the pointers to the first indexed elements in the respective matrices...
+ ia = offsetA;
+ ib = offsetB;
+
+ // Iterate over the matrix dimensions...
+ for ( i1 = 0; i1 < S1; i1++ ) {
+ for ( i0 = 0; i0 < S0; i0++ ) {
+ B[ ib ] = A[ ia ];
+ ia += da0;
+ ib += db0;
+ }
+ ia += da1;
+ ib += db1;
+ }
+ return 0;
+}
+
+/**
+* Copies the upper triangular/trapezoidal part of a matrix `A` to another matrix `B`.
+*
+* @param M number of rows in the matrix `A`
+* @param N number of columns in the matrix `A`
+* @param A input matrix
+* @param strideA1 stride of the first dimension of `A`
+* @param strideA2 stride of the second dimension of `A`
+* @param offsetA starting index for `A`
+* @param B output matrix
+* @param strideB1 stride of the first dimension of `B`
+* @param strideB2 stride of the second dimension of `B`
+* @param offsetB starting index for `B`
+* @return status code
+*/
+static LAPACK_INT stdlib_lapack_base_copy_upper( const LAPACK_INT M, const LAPACK_INT N, const double *A, const LAPACK_INT strideA1, const LAPACK_INT strideA2, const LAPACK_INT offsetA, double *B, const LAPACK_INT strideB1, const LAPACK_INT strideB2, const LAPACK_INT offsetB ) {
+ int64_t sa[ 2 ];
+ LAPACK_INT ia;
+ LAPACK_INT ib;
+ LAPACK_INT i0;
+ LAPACK_INT i1;
+
+ ia = offsetA;
+ ib = offsetB;
+
+ sa[ 0 ] = strideA1;
+ sa[ 1 ] = strideA2;
+ if ( stdlib_ndarray_is_row_major( 2, sa ) ) {
+ for ( i1 = 0; i1 < M; i1++ ) {
+ for ( i0 = i1; i0 < N; i0++ ) {
+ B[ ib+(i0*strideB2) ] = A[ ia+(i0*strideA2) ];
+ }
+ ia += strideA1;
+ ib += strideB1;
+ }
+ return 0;
+ }
+ for ( i1 = 0; i1 < N; i1++ ) {
+ for ( i0 = 0; i0 <= MIN( i1, M-1 ); i0++ ) {
+ B[ ib+(i0*strideB1) ] = A[ ia+(i0*strideA1) ];
+ }
+ ia += strideA2;
+ ib += strideB2;
+ }
+ return 0;
+}
+
+/**
+* Copies the lower triangular/trapezoidal part of a matrix `A` to another matrix `B`.
+*
+* @param M number of rows in the matrix `A`
+* @param N number of columns in the matrix `A`
+* @param A input matrix
+* @param strideA1 stride of the first dimension of `A`
+* @param strideA2 stride of the second dimension of `A`
+* @param offsetA starting index for `A`
+* @param B output matrix
+* @param strideB1 stride of the first dimension of `B`
+* @param strideB2 stride of the second dimension of `B`
+* @param offsetB starting index for `B`
+* @return status code
+*/
+static LAPACK_INT stdlib_lapack_base_copy_lower( const LAPACK_INT M, const LAPACK_INT N, const double *A, const LAPACK_INT strideA1, const LAPACK_INT strideA2, const LAPACK_INT offsetA, double *B, const LAPACK_INT strideB1, const LAPACK_INT strideB2, const LAPACK_INT offsetB ) {
+ int64_t sa[ 2 ];
+ LAPACK_INT ia;
+ LAPACK_INT ib;
+ LAPACK_INT i0;
+ LAPACK_INT i1;
+
+ ia = offsetA;
+ ib = offsetB;
+
+ sa[ 0 ] = strideA1;
+ sa[ 1 ] = strideA2;
+ if ( stdlib_ndarray_is_row_major( 2, sa ) ) {
+ for ( i1 = 0; i1 < M; i1++ ) {
+ for ( i0 = 0; i0 <= MIN( i1, N-1 ); i0++ ) {
+ B[ ib+(i0*strideB2) ] = A[ ia+(i0*strideA2) ];
+ }
+ ia += strideA1;
+ ib += strideB1;
+ }
+ return 0;
+ }
+ for ( i1 = 0; i1 < N; i1++ ) {
+ for ( i0 = i1; i0 < M; i0++ ) {
+ B[ ib+(i0*strideB1) ] = A[ ia+(i0*strideA1) ];
+ }
+ ia += strideA2;
+ ib += strideB2;
+ }
+ return 0;
+}
+
+/**
+* Copies all or part of a matrix `A` to another matrix `B` using alternative indexing semantics.
+*
+* @param uplo specifies whether to copy the upper or lower triangular/trapezoidal part of a matrix `A`
+* @param M number of rows in the matrix `A`
+* @param N number of columns in the matrix `A`
+* @param A input matrix
+* @param strideA1 stride of the first dimension of `A`
+* @param strideA2 stride of the second dimension of `A`
+* @param offsetA starting index for `A`
+* @param B output matrix
+* @param strideB1 stride of the first dimension of `B`
+* @param strideB2 stride of the second dimension of `B`
+* @param offsetB starting index for `B`
+* @return status code
+*/
+LAPACK_INT API_SUFFIX(c_dlacpy_ndarray)( const int uplo, const LAPACK_INT M, const LAPACK_INT N, const double *A, const LAPACK_INT strideA1, const LAPACK_INT strideA2, const LAPACK_INT offsetA, double *B, const LAPACK_INT strideB1, const LAPACK_INT strideB2, const LAPACK_INT offsetB ) {
+ if ( uplo == LAPACK_UPPER_TRIANGLE ) {
+ return stdlib_lapack_base_copy_upper( M, N, A, strideA1, strideA2, offsetA, B, strideB1, strideB2, offsetB );
+ }
+ if ( uplo == LAPACK_LOWER_TRIANGLE ) {
+ return stdlib_lapack_base_copy_lower( M, N, A, strideA1, strideA2, offsetA, B, strideB1, strideB2, offsetB );
+ }
+ return stdlib_lapack_base_copy_all( M, N, A, strideA1, strideA2, offsetA, B, strideB1, strideB2, offsetB );
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/test/test.dlacpy.native.js b/lib/node_modules/@stdlib/lapack/base/dlacpy/test/test.dlacpy.native.js
new file mode 100644
index 000000000000..d7cf9f6c3c86
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/test/test.dlacpy.native.js
@@ -0,0 +1,291 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var resolve = require( 'path' ).resolve;
+var tape = require( 'tape' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var Float64Array = require( '@stdlib/array/float64' );
+var tryRequire = require( '@stdlib/utils/try-require' );
+
+
+// VARIABLES //
+
+var dlacpy = tryRequire( resolve( __dirname, './../lib/dlacpy.native.js' ) );
+var opts = {
+ 'skip': ( dlacpy instanceof Error )
+};
+
+
+// TESTS //
+
+tape( 'main export is a function', opts, function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof dlacpy, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'the function has an arity of 8', opts, function test( t ) {
+ t.strictEqual( dlacpy.length, 8, 'returns expected value' );
+ t.end();
+});
+
+tape( 'the function throws an error if provided an invalid first argument', opts, function test( t ) {
+ var values;
+ var A;
+ var B;
+ var i;
+
+ values = [
+ 'foo',
+ 'bar',
+ 'beep',
+ 'boop'
+ ];
+
+ A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
+ B = new Float64Array( 4 );
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dlacpy( value, 'all', 2, 2, A, 2, B, 2 );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided an invalid sixth argument (row-major)', opts, function test( t ) {
+ var values;
+ var A;
+ var B;
+ var i;
+
+ values = [
+ 0,
+ 1
+ ];
+
+ A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
+ B = new Float64Array( 4 );
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dlacpy( 'row-major', 'all', 2, 2, A, value, B, 2 );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided an invalid eighth argument (row-major)', opts, function test( t ) {
+ var values;
+ var A;
+ var B;
+ var i;
+
+ values = [
+ 0,
+ 1
+ ];
+
+ A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
+ B = new Float64Array( 4 );
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dlacpy( 'row-major', 'all', 2, 2, A, 2, B, value );
+ };
+ }
+});
+
+tape( 'the function copies all of a matrix `A` to another matrix `B` (row-major)', opts, function test( t ) {
+ var opts;
+ var out;
+ var A;
+ var B;
+ var M;
+ var N;
+
+ M = 5;
+ N = 8;
+
+ opts = {
+ 'dtype': 'float64'
+ };
+
+ A = uniform( M*N, -10.0, 10.0, opts );
+ B = new Float64Array( M*N );
+
+ out = dlacpy( 'row-major', 'all', M, N, A, N, B, N );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, A, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function copies a part of a matrix `A` to another matrix `B` (row-major, upper)', opts, function test( t ) {
+ var opts;
+ var out;
+ var A;
+ var B;
+ var M;
+ var N;
+
+ M = 5;
+ N = 8;
+
+ opts = {
+ 'dtype': 'float64'
+ };
+
+ A = uniform( M*N, -10.0, 10.0, opts );
+ B = new Float64Array( M*N );
+
+ out = dlacpy( 'row-major', 'lower', M, N, A, N, B, N );
+ t.strictEqual( out, B, 'returns expected value' );
+
+ out = dlacpy( 'row-major', 'upper', M, N, A, N, B, N );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, A, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function copies a part of a matrix `A` to another matrix `B` (row-major, lower)', opts, function test( t ) {
+ var opts;
+ var out;
+ var A;
+ var B;
+ var M;
+ var N;
+
+ M = 5;
+ N = 8;
+
+ opts = {
+ 'dtype': 'float64'
+ };
+
+ A = uniform( M*N, -10.0, 10.0, opts );
+ B = new Float64Array( M*N );
+
+ out = dlacpy( 'row-major', 'upper', M, N, A, N, B, N );
+ t.strictEqual( out, B, 'returns expected value' );
+
+ out = dlacpy( 'row-major', 'lower', M, N, A, N, B, N );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, A, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function copies all of a matrix `A` to another matrix `B` (column-major)', opts, function test( t ) {
+ var opts;
+ var out;
+ var A;
+ var B;
+ var M;
+ var N;
+
+ M = 5;
+ N = 8;
+
+ opts = {
+ 'dtype': 'float64'
+ };
+
+ A = uniform( M*N, -10.0, 10.0, opts );
+ B = new Float64Array( M*N );
+
+ out = dlacpy( 'column-major', 'all', M, N, A, M, B, M );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, A, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function copies a part of a matrix `A` to another matrix `B` (column-major, upper)', opts, function test( t ) {
+ var opts;
+ var out;
+ var A;
+ var B;
+ var M;
+ var N;
+
+ M = 5;
+ N = 8;
+
+ opts = {
+ 'dtype': 'float64'
+ };
+
+ A = uniform( M*N, -10.0, 10.0, opts );
+ B = new Float64Array( M*N );
+
+ out = dlacpy( 'column-major', 'lower', M, N, A, M, B, M );
+ t.strictEqual( out, B, 'returns expected value' );
+
+ out = dlacpy( 'column-major', 'upper', M, N, A, M, B, M );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, A, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function copies a part of a matrix `A` to another matrix `B` (column-major, lower)', opts, function test( t ) {
+ var opts;
+ var out;
+ var A;
+ var B;
+ var M;
+ var N;
+
+ M = 5;
+ N = 8;
+
+ opts = {
+ 'dtype': 'float64'
+ };
+
+ A = uniform( M*N, -10.0, 10.0, opts );
+ B = new Float64Array( M*N );
+
+ out = dlacpy( 'column-major', 'upper', M, N, A, M, B, M );
+ t.strictEqual( out, B, 'returns expected value' );
+
+ out = dlacpy( 'column-major', 'lower', M, N, A, M, B, M );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, A, 'returns expected value' );
+
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/lapack/base/dlacpy/test/test.ndarray.native.js b/lib/node_modules/@stdlib/lapack/base/dlacpy/test/test.ndarray.native.js
new file mode 100644
index 000000000000..eba8be28f2d0
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/dlacpy/test/test.ndarray.native.js
@@ -0,0 +1,279 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2024 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+/* eslint-disable max-len */
+
+'use strict';
+
+// MODULES //
+
+var resolve = require( 'path' ).resolve;
+var tape = require( 'tape' );
+var Float64Array = require( '@stdlib/array/float64' );
+var tryRequire = require( '@stdlib/utils/try-require' );
+
+
+// VARIABLES //
+
+var dlacpy = tryRequire( resolve( __dirname, './../lib/ndarray.native.js' ) );
+var opts = {
+ 'skip': ( dlacpy instanceof Error )
+};
+
+
+// TESTS //
+
+tape( 'main export is a function', opts, function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof dlacpy, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'the function has an arity of 11', opts, function test( t ) {
+ t.strictEqual( dlacpy.length, 11, 'returns expected value' );
+ t.end();
+});
+
+tape( 'the function copies all of a matrix `A` to another matrix `B` (row-major)', opts, function test( t ) {
+ var expected;
+ var out;
+ var A;
+ var B;
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+
+ out = dlacpy( 'all', 2, 3, A, 3, 1, 1, B, 3, 1, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0 ] );
+
+ out = dlacpy( 'all', 2, 3, A, -3, -1, 6, B, 3, 1, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+
+ out = dlacpy( 'all', 3, 2, A, 2, 1, 1, B, 2, 1, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function copies part of a matrix `A` to another matrix `B` (row-major, upper)', opts, function test( t ) {
+ var expected;
+ var out;
+ var A;
+ var B;
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 11.0, 12.0, 13.0, 0.0, 15.0, 16.0 ] );
+
+ out = dlacpy( 'upper', 2, 3, A, 3, 1, 1, B, 3, 1, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 13.0, 12.0, 11.0, 0.0, 15.0, 14.0 ] );
+
+ out = dlacpy( 'upper', 2, 3, A, 3, -1, 3, B, 3, 1, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 11.0, 12.0, 0.0, 14.0, 0.0, 0.0 ] );
+
+ out = dlacpy( 'upper', 3, 2, A, 2, 1, 1, B, 2, 1, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function copies part of a matrix `A` to another matrix `B` (row-major, lower)', opts, function test( t ) {
+ var expected;
+ var out;
+ var A;
+ var B;
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 11.0, 0.0, 0.0, 14.0, 15.0, 0.0 ] );
+
+ out = dlacpy( 'lower', 2, 3, A, 3, 1, 1, B, 3, 1, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 14.0, 0.0, 0.0, 11.0, 12.0, 0.0 ] );
+
+ out = dlacpy( 'lower', 2, 3, A, -3, 1, 4, B, 3, 1, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 11.0, 0.0, 13.0, 14.0, 15.0, 16.0 ] );
+
+ out = dlacpy( 'lower', 3, 2, A, 2, 1, 1, B, 2, 1, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function copies all of a matrix `A` to another matrix `B` (column-major)', opts, function test( t ) {
+ var expected;
+ var out;
+ var A;
+ var B;
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+
+ out = dlacpy( 'all', 2, 3, A, 1, 2, 1, B, 1, 2, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0 ] );
+
+ out = dlacpy( 'all', 2, 3, A, 1, 2, 1, B, -1, -2, 8 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+
+ out = dlacpy( 'all', 3, 2, A, 1, 3, 1, B, 1, 3, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function copies part of a matrix `A` to another matrix `B` (column-major, upper)', opts, function test( t ) {
+ var expected;
+ var out;
+ var A;
+ var B;
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 11.0, 0.0, 13.0, 14.0, 15.0, 16.0 ] );
+
+ out = dlacpy( 'upper', 2, 3, A, 1, 2, 1, B, 1, 2, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 11.0, 14.0, 13.0, 16.0, 15.0 ] );
+
+ out = dlacpy( 'upper', 2, 3, A, 1, 2, 1, B, -1, 2, 4 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 11.0, 0.0, 0.0, 14.0, 15.0, 0.0 ] );
+
+ out = dlacpy( 'upper', 3, 2, A, 1, 3, 1, B, 1, 3, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function copies part of a matrix `A` to another matrix `B` (column-major, lower)', opts, function test( t ) {
+ var expected;
+ var out;
+ var A;
+ var B;
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 11.0, 12.0, 0.0, 14.0, 0.0, 0.0 ] );
+
+ out = dlacpy( 'lower', 2, 3, A, 1, 2, 1, B, 1, 2, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 11.0, 12.0 ] );
+
+ out = dlacpy( 'lower', 2, 3, A, 1, 2, 1, B, 1, -2, 7 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ A = new Float64Array( [ 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ] );
+ B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
+ expected = new Float64Array( [ 0.0, 0.0, 0.0, 11.0, 12.0, 13.0, 0.0, 15.0, 16.0 ] );
+
+ out = dlacpy( 'lower', 3, 2, A, 1, 3, 1, B, 1, 3, 3 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, expected, 'returns expected value' );
+
+ t.end();
+});
+
+tape( 'the function copies all of a matrix `A` to another matrix `B` (column-major, lower, complex access patterns)', opts, function test( t ) {
+ var out;
+ var A;
+ var B;
+
+ /* eslint-disable array-element-newline, no-multi-spaces */
+
+ A = new Float64Array([
+ 999, 999, 999, 999, 999, 999,
+ 999, 1, 999, 2, 999, 3,
+ 999, 0, 999, 4, 999, 5,
+ 999, 0, 999, 0, 999, 6,
+ 999, 999, 999, 999, 999, 999
+ ]);
+ B = new Float64Array([
+ 999, 999, 999, 999, 999, 999,
+ 999, 999, 999, 999, 999, 999,
+ 999, 999, 999, 999, 999, 999,
+ 999, 999, 999, 999, 999, 999,
+ 999, 999, 999, 999, 999, 999
+ ]);
+
+ /* eslint-enable array-element-newline, no-multi-spaces */
+
+ out = dlacpy( 'all', 3, 3, A, 2, 6, 7, B, 2, 6, 7 );
+ t.strictEqual( out, B, 'returns expected value' );
+ t.deepEqual( out, A, 'returns expected value' );
+
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/README.md b/lib/node_modules/@stdlib/lapack/base/shared/README.md
new file mode 100644
index 000000000000..5dbeffb66e7f
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/README.md
@@ -0,0 +1,157 @@
+
+
+# shared
+
+> Common symbols and type definitions shared across base LAPACK APIs.
+
+
+
+
+
+
+
+
+
+
+
+## Usage
+
+```javascript
+var headerDir = require( '@stdlib/lapack/base/shared' );
+```
+
+#### headerDir
+
+Absolute file path for the directory containing header files for C APIs.
+
+```javascript
+var dir = headerDir;
+// returns
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Examples
+
+```javascript
+var headerDir = require( '@stdlib/lapack/base/shared' );
+
+console.log( headerDir );
+// =>
+```
+
+
+
+
+
+
+
+* * *
+
+
+
+## C APIs
+
+
+
+
+
+
+
+
+
+
+
+### Usage
+
+```c
+#include "stdlib/lapack/base/shared.h"
+```
+
+#### TODO
+
+TODO
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/docs/types/index.d.ts b/lib/node_modules/@stdlib/lapack/base/shared/docs/types/index.d.ts
new file mode 100644
index 000000000000..fd6d90004849
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/docs/types/index.d.ts
@@ -0,0 +1,33 @@
+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+// TypeScript Version: 4.1
+
+/**
+* Absolute file path for the directory containing header files for C APIs.
+*
+* @example
+* var dir = headerDir;
+* // returns
+*/
+declare const headerDir: string;
+
+
+// EXPORTS //
+
+export = headerDir;
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/docs/types/test.ts b/lib/node_modules/@stdlib/lapack/base/shared/docs/types/test.ts
new file mode 100644
index 000000000000..d7e59543e51e
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/docs/types/test.ts
@@ -0,0 +1,28 @@
+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+import headerDir = require( './index' );
+
+
+// TESTS //
+
+// The variable is a string...
+{
+ // eslint-disable-next-line @typescript-eslint/no-unused-expressions
+ headerDir; // $ExpectType string
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/examples/index.js b/lib/node_modules/@stdlib/lapack/base/shared/examples/index.js
new file mode 100644
index 000000000000..b0b1fa522f05
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/examples/index.js
@@ -0,0 +1,24 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+var headerDir = require( './../lib' );
+
+console.log( headerDir );
+// =>
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/include/stdlib/lapack/base/shared.h b/lib/node_modules/@stdlib/lapack/base/shared/include/stdlib/lapack/base/shared.h
new file mode 100644
index 000000000000..a371efb45808
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/include/stdlib/lapack/base/shared.h
@@ -0,0 +1,25 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#ifndef STDLIB_LAPACK_BASE_SHARED_H
+#define STDLIB_LAPACK_BASE_SHARED_H
+
+#include "shared/lapacke.h"
+#include "shared/macros.h"
+
+#endif // !STDLIB_LAPACK_BASE_SHARED_H
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/include/stdlib/lapack/base/shared/lapacke.h b/lib/node_modules/@stdlib/lapack/base/shared/include/stdlib/lapack/base/shared/lapacke.h
new file mode 100644
index 000000000000..5629e9e0a116
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/include/stdlib/lapack/base/shared/lapacke.h
@@ -0,0 +1,124 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#ifndef STDLIB_LAPACK_BASE_SHARED_LAPACKE_H
+#define STDLIB_LAPACK_BASE_SHARED_LAPACKE_H
+
+#include "stdlib/blas/base/shared.h"
+#include
+#include
+#include
+
+/*
+* If C++, prevent name mangling so that the compiler emits a binary file having undecorated names, thus mirroring the behavior of a C compiler.
+*/
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// Integer type:
+#ifndef LAPACK_INT
+#ifdef LAPACK_ILP64
+#define LAPACK_INT int64_t
+#else
+#define LAPACK_INT int32_t
+#endif
+#endif
+
+// Integer format string:
+#ifndef LAPACK_IFMT
+#ifdef LAPACK_ILP64
+#define LAPACK_IFMT PRId64
+#else
+#define LAPACK_IFMT PRId32
+#endif
+#endif
+
+// Array storage layout:
+#ifndef LAPACK_LAYOUT
+typedef enum LAPACK_LAYOUT {
+ // Row-major order (C-style):
+ LAPACK_ROW_MAJOR = CblasRowMajor,
+
+ // Column-major order (Fortran-style):
+ LAPACK_COL_MAJOR = CblasColMajor,
+} LAPACK_LAYOUT;
+#endif
+
+// Transpose operation:
+#ifndef LAPACK_TRANSPOSITION
+typedef enum LAPACK_TRANSPOSE {
+ // No transposition:
+ LAPACK_NO_TRANSPOSE = CblasNoTrans,
+
+ // Transposition:
+ LAPACK_TRANSPOSE = CblasTrans,
+
+ // Conjugate transposition:
+ LAPACK_CONJUGATE_TRANSPOSE = CblasConjTrans,
+} LAPACK_TRANSPOSITION;
+#endif
+
+// Upper/lower triangular part:
+#ifndef LAPACK_UPLO
+typedef enum LAPACK_UPLO {
+ // Upper triangular part:
+ LAPACK_UPPER_TRIANGLE = CblasUpper,
+
+ // Lower triangular part:
+ LAPACK_LOWER_TRIANGLE = CblasLower,
+} LAPACK_UPLO;
+#endif
+
+// Diagonal elements:
+#ifndef LAPACK_DIAGONAL
+typedef enum LAPACK_DIAGONAL {
+ // Non-unit diagonal elements:
+ LAPACK_NON_UNIT_DIAGONAL = CblasNonUnit,
+
+ // Unit diagonal elements:
+ LAPACK_UNIT_DIAGONAL = CblasUnit,
+} LAPACK_DIAGONAL;
+#endif
+
+// Operation side:
+#ifndef LAPACK_SIDE
+typedef enum LAPACK_SIDE {
+ // Triangular matrix is on the left side of a matrix-matrix operation (e.g., AX = B, where A is a triangular matrix):
+ LAPACK_LEFT = CblasLeft,
+
+ // Triangular matrix is on the right side of a matrix-matrix operation (e.g., XA = B, where A is a triangular matrix):
+ LAPACK_RIGHT = CblasRight,
+} LAPACK_SIDE;
+#endif
+
+// Memory error code when unable to allocate a workspace array:
+#ifndef LAPACK_WORK_MEMORY_ERROR
+#define LAPACK_WORK_MEMORY_ERROR -1010
+#endif
+
+// Memory error code when unable to allocate memory when transposing an array:
+#ifndef LAPACK_TRANSPOSE_MEMORY_ERROR
+#define LAPACK_TRANSPOSE_MEMORY_ERROR -1011
+#endif
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // !STDLIB_LAPACK_BASE_SHARED_LAPACKE_H
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/include/stdlib/lapack/base/shared/macros.h b/lib/node_modules/@stdlib/lapack/base/shared/include/stdlib/lapack/base/shared/macros.h
new file mode 100644
index 000000000000..5dda11f80635
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/include/stdlib/lapack/base/shared/macros.h
@@ -0,0 +1,35 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#ifndef STDLIB_LAPACKE_BASE_SHARED_MACROS_H
+#define STDLIB_LAPACKE_BASE_SHARED_MACROS_H
+
+/*
+* If C++, prevent name mangling so that the compiler emits a binary file having undecorated names, thus mirroring the behavior of a C compiler.
+*/
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// TODO: add macros
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // !STDLIB_LAPACKE_BASE_SHARED_MACROS_H
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/lib/browser.js b/lib/node_modules/@stdlib/lapack/base/shared/lib/browser.js
new file mode 100644
index 000000000000..104fe93e3a6c
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/lib/browser.js
@@ -0,0 +1,28 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MAIN //
+
+var dir = null;
+
+
+// EXPORTS //
+
+module.exports = dir;
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/lib/index.js b/lib/node_modules/@stdlib/lapack/base/shared/lib/index.js
new file mode 100644
index 000000000000..041e2baa58ca
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/lib/index.js
@@ -0,0 +1,39 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+/**
+* Absolute file path for the directory containing header files for C APIs.
+*
+* @module @stdlib/lapack/base/shared
+*
+* @example
+* var headerDir = require( '@stdlib/lapack/base/shared' );
+*
+* console.log( headerDir );
+*/
+
+// MODULES //
+
+var main = require( './main.js' );
+
+
+// EXPORTS //
+
+module.exports = main;
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/lib/main.js b/lib/node_modules/@stdlib/lapack/base/shared/lib/main.js
new file mode 100644
index 000000000000..d09f5d231f4d
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/lib/main.js
@@ -0,0 +1,33 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var resolve = require( 'path' ).resolve;
+
+
+// MAIN //
+
+var dir = resolve( __dirname, '..', 'include' );
+
+
+// EXPORTS //
+
+module.exports = dir;
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/manifest.json b/lib/node_modules/@stdlib/lapack/base/shared/manifest.json
new file mode 100644
index 000000000000..257e4d556afb
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/manifest.json
@@ -0,0 +1,40 @@
+{
+ "options": {},
+ "fields": [
+ {
+ "field": "src",
+ "resolve": true,
+ "relative": true
+ },
+ {
+ "field": "include",
+ "resolve": true,
+ "relative": true
+ },
+ {
+ "field": "libraries",
+ "resolve": false,
+ "relative": false
+ },
+ {
+ "field": "libpath",
+ "resolve": true,
+ "relative": false
+ }
+ ],
+ "confs": [
+ {
+ "src": [
+ "./src/main.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/blas/base/shared"
+ ]
+ }
+ ]
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/package.json b/lib/node_modules/@stdlib/lapack/base/shared/package.json
new file mode 100644
index 000000000000..81ed9e8c523c
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/package.json
@@ -0,0 +1,67 @@
+{
+ "name": "@stdlib/lapack/base/shared",
+ "version": "0.0.0",
+ "description": "Common symbols and type definitions shared across base LAPACK APIs.",
+ "license": "Apache-2.0",
+ "author": {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ },
+ "contributors": [
+ {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ }
+ ],
+ "main": "./lib",
+ "directories": {
+ "doc": "./docs",
+ "example": "./examples",
+ "include": "./include",
+ "lib": "./lib",
+ "src": "./src",
+ "test": "./test"
+ },
+ "types": "./docs/types",
+ "scripts": {},
+ "homepage": "https://github.com/stdlib-js/stdlib",
+ "repository": {
+ "type": "git",
+ "url": "git://github.com/stdlib-js/stdlib.git"
+ },
+ "bugs": {
+ "url": "https://github.com/stdlib-js/stdlib/issues"
+ },
+ "dependencies": {},
+ "devDependencies": {},
+ "engines": {
+ "node": ">=0.10.0",
+ "npm": ">2.7.0"
+ },
+ "os": [
+ "aix",
+ "darwin",
+ "freebsd",
+ "linux",
+ "macos",
+ "openbsd",
+ "sunos",
+ "win32",
+ "windows"
+ ],
+ "keywords": [
+ "stdlib",
+ "standard",
+ "library",
+ "std",
+ "lib",
+ "base",
+ "lapack",
+ "c",
+ "shared",
+ "common"
+ ],
+ "__stdlib__": {
+ "wasm": false
+ }
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/src/main.c b/lib/node_modules/@stdlib/lapack/base/shared/src/main.c
new file mode 100644
index 000000000000..bd899d44d81f
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/src/main.c
@@ -0,0 +1,19 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#include "stdlib/lapack/base/shared.h"
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/test/test.browser.js b/lib/node_modules/@stdlib/lapack/base/shared/test/test.browser.js
new file mode 100644
index 000000000000..ed4a1e26b956
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/test/test.browser.js
@@ -0,0 +1,33 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var tape = require( 'tape' );
+var headerDir = require( './../lib/browser.js' );
+
+
+// TESTS //
+
+tape( 'main export is null', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( headerDir, null, 'main export is null' );
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/lapack/base/shared/test/test.js b/lib/node_modules/@stdlib/lapack/base/shared/test/test.js
new file mode 100644
index 000000000000..3efe6d8d766c
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/shared/test/test.js
@@ -0,0 +1,48 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var resolve = require( 'path' ).resolve;
+var tape = require( 'tape' );
+var IS_BROWSER = require( '@stdlib/assert/is-browser' );
+var headerDir = require( './../lib' );
+
+
+// VARIABLES //
+
+var opts = {
+ 'skip': IS_BROWSER
+};
+
+
+// TESTS //
+
+tape( 'main export is a string', opts, function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof headerDir, 'string', 'main export is a string' );
+ t.end();
+});
+
+tape( 'the exported value corresponds to the package directory containing header files', opts, function test( t ) {
+ var dir = resolve( __dirname, '..', 'include' );
+ t.strictEqual( headerDir, dir, 'exports expected value' );
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/README.md b/lib/node_modules/@stdlib/lapack/base/xerbla/README.md
new file mode 100644
index 000000000000..9ba212ea858c
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/README.md
@@ -0,0 +1,172 @@
+
+
+# xerbla
+
+> Error handler for LAPACK routines.
+
+
+
+
+
+
+
+
+
+
+
+## Usage
+
+```javascript
+var headerDir = require( '@stdlib/lapack/base/xerbla' );
+```
+
+#### headerDir
+
+Absolute file path for the directory containing header files for C APIs.
+
+```javascript
+var dir = headerDir;
+// returns
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Examples
+
+```javascript
+var headerDir = require( '@stdlib/lapack/base/xerbla' );
+
+console.log( headerDir );
+// =>
+```
+
+
+
+
+
+
+
+* * *
+
+
+
+## C APIs
+
+
+
+
+
+
+
+
+
+
+
+### Usage
+
+```c
+#include "stdlib/lapack/base/xerbla.h"
+```
+
+#### lapack_xerbla( \*name, info )
+
+Prints an error message.
+
+
+
+```c
+lapack_xerbla( "dlacpy", 1 )
+```
+
+The function accepts the following arguments:
+
+- **name**: `[in] char*` name of routine (e.g., `dlacpy`).
+- **info**: `[in] LAPACK_INT` error code (e.g., a negative argument index).
+
+```c
+void lapack_xerbla( const char *name, LAPACK_INT info );
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/binding.gyp b/lib/node_modules/@stdlib/lapack/base/xerbla/binding.gyp
new file mode 100644
index 000000000000..08de71a2020e
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/binding.gyp
@@ -0,0 +1,265 @@
+# @license Apache-2.0
+#
+# Copyright (c) 2025 The Stdlib Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# A `.gyp` file for building a Node.js native add-on.
+#
+# [1]: https://gyp.gsrc.io/docs/InputFormatReference.md
+# [2]: https://gyp.gsrc.io/docs/UserDocumentation.md
+{
+ # List of files to include in this file:
+ 'includes': [
+ './include.gypi',
+ ],
+
+ # Define variables to be used throughout the configuration for all targets:
+ 'variables': {
+ # Target name should match the add-on export name:
+ 'addon_target_name%': 'addon',
+
+ # Fortran compiler (to override -Dfortran_compiler=):
+ 'fortran_compiler%': 'gfortran',
+
+ # Fortran compiler flags:
+ 'fflags': [
+ # Specify the Fortran standard to which a program is expected to conform:
+ '-std=f95',
+
+ # Indicate that the layout is free-form source code:
+ '-ffree-form',
+
+ # Aggressive optimization:
+ '-O3',
+
+ # Enable commonly used warning options:
+ '-Wall',
+
+ # Warn if source code contains problematic language features:
+ '-Wextra',
+
+ # Warn if a procedure is called without an explicit interface:
+ '-Wimplicit-interface',
+
+ # Do not transform names of entities specified in Fortran source files by appending underscores (i.e., don't mangle names, thus allowing easier usage in C wrappers):
+ '-fno-underscoring',
+
+ # Warn if source code contains Fortran 95 extensions and C-language constructs:
+ '-pedantic',
+
+ # Compile but do not link (output is an object file):
+ '-c',
+ ],
+
+ # Set variables based on the host OS:
+ 'conditions': [
+ [
+ 'OS=="win"',
+ {
+ # Define the object file suffix:
+ 'obj': 'obj',
+ },
+ {
+ # Define the object file suffix:
+ 'obj': 'o',
+ }
+ ], # end condition (OS=="win")
+ ], # end conditions
+ }, # end variables
+
+ # Define compile targets:
+ 'targets': [
+
+ # Target to generate an add-on:
+ {
+ # The target name should match the add-on export name:
+ 'target_name': '<(addon_target_name)',
+
+ # Define dependencies:
+ 'dependencies': [],
+
+ # Define directories which contain relevant include headers:
+ 'include_dirs': [
+ # Local include directory:
+ '<@(include_dirs)',
+ ],
+
+ # List of source files:
+ 'sources': [
+ '<@(src_files)',
+ ],
+
+ # Settings which should be applied when a target's object files are used as linker input:
+ 'link_settings': {
+ # Define libraries:
+ 'libraries': [
+ '<@(libraries)',
+ ],
+
+ # Define library directories:
+ 'library_dirs': [
+ '<@(library_dirs)',
+ ],
+ },
+
+ # C/C++ compiler flags:
+ 'cflags': [
+ # Enable commonly used warning options:
+ '-Wall',
+
+ # Aggressive optimization:
+ '-O3',
+ ],
+
+ # C specific compiler flags:
+ 'cflags_c': [
+ # Specify the C standard to which a program is expected to conform:
+ '-std=c99',
+ ],
+
+ # C++ specific compiler flags:
+ 'cflags_cpp': [
+ # Specify the C++ standard to which a program is expected to conform:
+ '-std=c++11',
+ ],
+
+ # Linker flags:
+ 'ldflags': [],
+
+ # Apply conditions based on the host OS:
+ 'conditions': [
+ [
+ 'OS=="mac"',
+ {
+ # Linker flags:
+ 'ldflags': [
+ '-undefined dynamic_lookup',
+ '-Wl,-no-pie',
+ '-Wl,-search_paths_first',
+ ],
+ },
+ ], # end condition (OS=="mac")
+ [
+ 'OS!="win"',
+ {
+ # C/C++ flags:
+ 'cflags': [
+ # Generate platform-independent code:
+ '-fPIC',
+ ],
+ },
+ ], # end condition (OS!="win")
+ ], # end conditions
+
+ # Define custom build actions for particular inputs:
+ 'rules': [
+ {
+ # Define a rule for processing Fortran files:
+ 'extension': 'f',
+
+ # Define the pathnames to be used as inputs when performing processing:
+ 'inputs': [
+ # Full path of the current input:
+ '<(RULE_INPUT_PATH)'
+ ],
+
+ # Define the outputs produced during processing:
+ 'outputs': [
+ # Store an output object file in a directory for placing intermediate results (only accessible within a single target):
+ '<(INTERMEDIATE_DIR)/<(RULE_INPUT_ROOT).<(obj)'
+ ],
+
+ # Define the rule for compiling Fortran based on the host OS:
+ 'conditions': [
+ [
+ 'OS=="win"',
+
+ # Rule to compile Fortran on Windows:
+ {
+ 'rule_name': 'compile_fortran_windows',
+ 'message': 'Compiling Fortran file <(RULE_INPUT_PATH) on Windows...',
+
+ 'process_outputs_as_sources': 0,
+
+ # Define the command-line invocation:
+ 'action': [
+ '<(fortran_compiler)',
+ '<@(fflags)',
+ '<@(_inputs)',
+ '-o',
+ '<@(_outputs)',
+ ],
+ },
+
+ # Rule to compile Fortran on non-Windows:
+ {
+ 'rule_name': 'compile_fortran_linux',
+ 'message': 'Compiling Fortran file <(RULE_INPUT_PATH) on Linux...',
+
+ 'process_outputs_as_sources': 1,
+
+ # Define the command-line invocation:
+ 'action': [
+ '<(fortran_compiler)',
+ '<@(fflags)',
+ '-fPIC', # generate platform-independent code
+ '<@(_inputs)',
+ '-o',
+ '<@(_outputs)',
+ ],
+ }
+ ], # end condition (OS=="win")
+ ], # end conditions
+ }, # end rule (extension=="f")
+ ], # end rules
+ }, # end target <(addon_target_name)
+
+ # Target to copy a generated add-on to a standard location:
+ {
+ 'target_name': 'copy_addon',
+
+ # Declare that the output of this target is not linked:
+ 'type': 'none',
+
+ # Define dependencies:
+ 'dependencies': [
+ # Require that the add-on be generated before building this target:
+ '<(addon_target_name)',
+ ],
+
+ # Define a list of actions:
+ 'actions': [
+ {
+ 'action_name': 'copy_addon',
+ 'message': 'Copying addon...',
+
+ # Explicitly list the inputs in the command-line invocation below:
+ 'inputs': [],
+
+ # Declare the expected outputs:
+ 'outputs': [
+ '<(addon_output_dir)/<(addon_target_name).node',
+ ],
+
+ # Define the command-line invocation:
+ 'action': [
+ 'cp',
+ '<(PRODUCT_DIR)/<(addon_target_name).node',
+ '<(addon_output_dir)/<(addon_target_name).node',
+ ],
+ },
+ ], # end actions
+ }, # end target copy_addon
+ ], # end targets
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/docs/types/index.d.ts b/lib/node_modules/@stdlib/lapack/base/xerbla/docs/types/index.d.ts
new file mode 100644
index 000000000000..fd6d90004849
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/docs/types/index.d.ts
@@ -0,0 +1,33 @@
+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+// TypeScript Version: 4.1
+
+/**
+* Absolute file path for the directory containing header files for C APIs.
+*
+* @example
+* var dir = headerDir;
+* // returns
+*/
+declare const headerDir: string;
+
+
+// EXPORTS //
+
+export = headerDir;
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/docs/types/test.ts b/lib/node_modules/@stdlib/lapack/base/xerbla/docs/types/test.ts
new file mode 100644
index 000000000000..d7e59543e51e
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/docs/types/test.ts
@@ -0,0 +1,28 @@
+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+import headerDir = require( './index' );
+
+
+// TESTS //
+
+// The variable is a string...
+{
+ // eslint-disable-next-line @typescript-eslint/no-unused-expressions
+ headerDir; // $ExpectType string
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/examples/index.js b/lib/node_modules/@stdlib/lapack/base/xerbla/examples/index.js
new file mode 100644
index 000000000000..b0b1fa522f05
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/examples/index.js
@@ -0,0 +1,24 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+var headerDir = require( './../lib' );
+
+console.log( headerDir );
+// =>
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/examples/native.js.txt b/lib/node_modules/@stdlib/lapack/base/xerbla/examples/native.js.txt
new file mode 100644
index 000000000000..d25892edc543
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/examples/native.js.txt
@@ -0,0 +1,23 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+var xerbla = require( './../lib/native.js' );
+
+xerbla();
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/include.gypi b/lib/node_modules/@stdlib/lapack/base/xerbla/include.gypi
new file mode 100644
index 000000000000..4217944b5d20
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/include.gypi
@@ -0,0 +1,70 @@
+# @license Apache-2.0
+#
+# Copyright (c) 2025 The Stdlib Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# A GYP include file for building a Node.js native add-on.
+#
+# Note that nesting variables is required due to how GYP processes a configuration. Any variables defined within a nested 'variables' section is defined in the outer scope. Thus, conditions in the outer variable scope are free to use these variables without running into "variable undefined" errors.
+#
+# Main documentation:
+#
+# [1]: https://gyp.gsrc.io/docs/InputFormatReference.md
+# [2]: https://gyp.gsrc.io/docs/UserDocumentation.md
+#
+# Variable nesting hacks:
+#
+# [3]: https://chromium.googlesource.com/external/skia/gyp/+/master/common_variables.gypi
+# [4]: https://src.chromium.org/viewvc/chrome/trunk/src/build/common.gypi?revision=127004
+{
+ # Define variables to be used throughout the configuration for all targets:
+ 'variables': {
+ 'variables': {
+ # Host BLAS library (to override -Dblas=):
+ 'blas%': '',
+
+ # Path to BLAS library (to override -Dblas_dir=):
+ 'blas_dir%': '',
+ }, # end variables
+
+ # Source directory:
+ 'src_dir': './src',
+
+ # Include directories:
+ 'include_dirs': [
+ '<@(blas_dir)',
+ '=0.10.0",
+ "npm": ">2.7.0"
+ },
+ "os": [
+ "aix",
+ "darwin",
+ "freebsd",
+ "linux",
+ "macos",
+ "openbsd",
+ "sunos",
+ "win32",
+ "windows"
+ ],
+ "keywords": [
+ "stdlib",
+ "stdmath",
+ "mathematics",
+ "math",
+ "blas",
+ "level 1",
+ "linear",
+ "algebra",
+ "subroutines",
+ "error",
+ "handler",
+ "exception"
+ ],
+ "__stdlib__": {
+ "wasm": false
+ }
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/src/Makefile b/lib/node_modules/@stdlib/lapack/base/xerbla/src/Makefile
new file mode 100644
index 000000000000..7733b6180cb4
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/src/Makefile
@@ -0,0 +1,70 @@
+#/
+# @license Apache-2.0
+#
+# Copyright (c) 2025 The Stdlib Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#/
+
+# VARIABLES #
+
+ifndef VERBOSE
+ QUIET := @
+else
+ QUIET :=
+endif
+
+# Determine the OS ([1][1], [2][2]).
+#
+# [1]: https://en.wikipedia.org/wiki/Uname#Examples
+# [2]: http://stackoverflow.com/a/27776822/2225624
+OS ?= $(shell uname)
+ifneq (, $(findstring MINGW,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring MSYS,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring CYGWIN,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring Windows_NT,$(OS)))
+ OS := WINNT
+endif
+endif
+endif
+endif
+
+
+# RULES #
+
+#/
+# Removes generated files for building an add-on.
+#
+# @example
+# make clean-addon
+#/
+clean-addon:
+ $(QUIET) -rm -f *.o *.node
+
+.PHONY: clean-addon
+
+#/
+# Removes generated files.
+#
+# @example
+# make clean
+#/
+clean: clean-addon
+
+.PHONY: clean
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/src/addon.c b/lib/node_modules/@stdlib/lapack/base/xerbla/src/addon.c
new file mode 100644
index 000000000000..a89de009b39a
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/src/addon.c
@@ -0,0 +1,35 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#include "stdlib/lapack/base/xerbla.h"
+#include "stdlib/napi/export.h"
+#include
+
+/**
+* Receives JavaScript callback invocation data.
+*
+* @param env environment under which the function is invoked
+* @param info callback data
+* @return Node-API value
+*/
+static napi_value addon( napi_env env, napi_callback_info info ) {
+ lapack_xerbla( "c_dlacpy", "" ); // test values
+ return NULL;
+}
+
+STDLIB_NAPI_MODULE_EXPORT_FCN( addon )
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/src/xerbla.c b/lib/node_modules/@stdlib/lapack/base/xerbla/src/xerbla.c
new file mode 100644
index 000000000000..1c0466c9e4ab
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/src/xerbla.c
@@ -0,0 +1,37 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#include "stdlib/lapack/base/xerbla.h"
+#include "stdlib/lapack/base/shared.h"
+#include
+
+/**
+* Prints an error message.
+*
+* @param name routine name (e.g., "dlacpy")
+* @param info error code (e.g., a negative argument index)
+*/
+void API_SUFFIX(lapack_xerbla)( const char *name, LAPACK_INT info ) {
+ if( info == LAPACK_WORK_MEMORY_ERROR ) {
+ printf( "Error: insufficient memory. Not enough memory to allocate work array in %s.\n", name );
+ } else if( info == LAPACK_TRANSPOSE_MEMORY_ERROR ) {
+ printf( "Error: insufficient memory. Not enough memory to transpose matrix in %s.\n", name );
+ } else if( info < 0 ) {
+ printf( "Error: invalid argument. Must provide a valid argument to routine `%s`. Argument: %" LAPACK_IFMT ".\n", name, -info );
+ }
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/src/xerbla_lapacke.c b/lib/node_modules/@stdlib/lapack/base/xerbla/src/xerbla_lapacke.c
new file mode 100644
index 000000000000..9916ee2e31f3
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/src/xerbla_lapacke.c
@@ -0,0 +1,30 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+#include "stdlib/lapack/base/xerbla_lapacke.h"
+#include "stdlib/lapack/base/shared.h"
+
+/**
+* Prints an error message.
+*
+* @param name routine name (e.g., "dlacpy")
+* @param info error code (e.g., a negative argument index)
+*/
+void API_SUFFIX(lapack_xerbla)( const char *name, LAPACK_INT info ) {
+ API_SUFFIX(LAPACKE_xerbla)( name, info );
+}
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/test/test.browser.js b/lib/node_modules/@stdlib/lapack/base/xerbla/test/test.browser.js
new file mode 100644
index 000000000000..ed4a1e26b956
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/test/test.browser.js
@@ -0,0 +1,33 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var tape = require( 'tape' );
+var headerDir = require( './../lib/browser.js' );
+
+
+// TESTS //
+
+tape( 'main export is null', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( headerDir, null, 'main export is null' );
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/lapack/base/xerbla/test/test.js b/lib/node_modules/@stdlib/lapack/base/xerbla/test/test.js
new file mode 100644
index 000000000000..3efe6d8d766c
--- /dev/null
+++ b/lib/node_modules/@stdlib/lapack/base/xerbla/test/test.js
@@ -0,0 +1,48 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var resolve = require( 'path' ).resolve;
+var tape = require( 'tape' );
+var IS_BROWSER = require( '@stdlib/assert/is-browser' );
+var headerDir = require( './../lib' );
+
+
+// VARIABLES //
+
+var opts = {
+ 'skip': IS_BROWSER
+};
+
+
+// TESTS //
+
+tape( 'main export is a string', opts, function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof headerDir, 'string', 'main export is a string' );
+ t.end();
+});
+
+tape( 'the exported value corresponds to the package directory containing header files', opts, function test( t ) {
+ var dir = resolve( __dirname, '..', 'include' );
+ t.strictEqual( headerDir, dir, 'exports expected value' );
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/math/base/special/kernel-log1p/package.json b/lib/node_modules/@stdlib/math/base/special/kernel-log1p/package.json
index 5e948cc08204..199475aea1be 100644
--- a/lib/node_modules/@stdlib/math/base/special/kernel-log1p/package.json
+++ b/lib/node_modules/@stdlib/math/base/special/kernel-log1p/package.json
@@ -1,7 +1,7 @@
{
"name": "@stdlib/math/base/special/kernel-log1p",
"version": "0.0.0",
- "description": "Evaluates a correction term for double-precision base-2 and base-10 logarithms when 1+f is in [√2/2, √2].",
+ "description": "Evaluate a correction term for double-precision base-2 and base-10 logarithms when 1+f is in [√2/2, √2].",
"license": "Apache-2.0",
"author": {
"name": "The Stdlib Authors",
diff --git a/lib/node_modules/@stdlib/math/base/tools/normhermitepolyf/test/fixtures/python/medium_positive_5.json b/lib/node_modules/@stdlib/math/base/tools/normhermitepolyf/test/fixtures/python/medium_positive_5.json
index f0c443a85115..8694e69c9fda 100644
--- a/lib/node_modules/@stdlib/math/base/tools/normhermitepolyf/test/fixtures/python/medium_positive_5.json
+++ b/lib/node_modules/@stdlib/math/base/tools/normhermitepolyf/test/fixtures/python/medium_positive_5.json
@@ -1 +1 @@
-{"n": 5, "x": [1.0, 1.7094894647598267, 2.4189789295196533, 3.1284685134887695, 3.8379578590393066, 4.547447681427002, 5.256937026977539, 5.966426372528076, 6.675915718078613, 7.385405540466309, 8.094895362854004, 8.804384231567383, 9.513874053955078, 10.223362922668457, 10.932852745056152, 11.642342567443848, 12.351831436157227, 13.061321258544922, 13.770811080932617, 14.480299949645996, 15.189789772033691, 15.899279594421387, 16.608768463134766, 17.31825828552246, 18.027748107910156, 18.73723793029785, 19.446725845336914, 20.15621566772461, 20.865705490112305, 21.5751953125, 22.284685134887695, 22.99417495727539, 23.703662872314453, 24.41315269470215, 25.122642517089844, 25.83213233947754, 26.541622161865234, 27.25111198425293, 27.960599899291992, 28.670089721679688, 29.379579544067383, 30.089069366455078, 30.798559188842773, 31.508047103881836, 32.21753692626953, 32.92702865600586, 33.63651657104492, 34.346004486083984, 35.05549621582031, 35.764984130859375, 36.4744758605957, 37.183963775634766, 37.89345169067383, 38.602943420410156, 39.31243133544922, 40.02192306518555, 40.73141098022461, 41.44090270996094, 42.150390625, 42.85987854003906, 43.56937026977539, 44.27885818481445, 44.98834991455078, 45.697837829589844, 46.407325744628906, 47.116817474365234, 47.8263053894043, 48.535797119140625, 49.24528503417969, 49.95477294921875, 50.66426467895508, 51.37375259399414, 52.08324432373047, 52.79273223876953, 53.50222396850586, 54.21171188354492, 54.921199798583984, 55.63069152832031, 56.340179443359375, 57.0496711730957, 57.759159088134766, 58.46864700317383, 59.178138732910156, 59.88762664794922, 60.59711837768555, 61.30660629272461, 62.01609420776367, 62.7255859375, 63.43507385253906, 64.14456176757812, 64.85405731201172, 65.56354522705078, 66.27303314208984, 66.9825210571289, 67.69200897216797, 68.40150451660156, 69.11099243164062, 69.82048034667969, 70.52996826171875, 71.23945617675781, 71.9489517211914, 72.65843963623047, 73.36792755126953, 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diff --git a/lib/node_modules/@stdlib/namespace/alias2pkg/data/data.csv b/lib/node_modules/@stdlib/namespace/alias2pkg/data/data.csv
index 14e86f701e21..14c4c5ff2719 100644
--- a/lib/node_modules/@stdlib/namespace/alias2pkg/data/data.csv
+++ b/lib/node_modules/@stdlib/namespace/alias2pkg/data/data.csv
@@ -1430,12 +1430,12 @@ base.strided.ssumkbn2,"@stdlib/blas/ext/base/ssumkbn2"
base.strided.ssumors,"@stdlib/blas/ext/base/ssumors"
base.strided.ssumpw,"@stdlib/blas/ext/base/ssumpw"
base.strided.sswap,"@stdlib/blas/base/sswap"
-base.strided.stdev,"@stdlib/stats/base/stdev"
-base.strided.stdevch,"@stdlib/stats/base/stdevch"
-base.strided.stdevpn,"@stdlib/stats/base/stdevpn"
-base.strided.stdevtk,"@stdlib/stats/base/stdevtk"
-base.strided.stdevwd,"@stdlib/stats/base/stdevwd"
-base.strided.stdevyc,"@stdlib/stats/base/stdevyc"
+base.strided.stdev,"@stdlib/stats/strided/stdev"
+base.strided.stdevch,"@stdlib/stats/strided/stdevch"
+base.strided.stdevpn,"@stdlib/stats/strided/stdevpn"
+base.strided.stdevtk,"@stdlib/stats/strided/stdevtk"
+base.strided.stdevwd,"@stdlib/stats/strided/stdevwd"
+base.strided.stdevyc,"@stdlib/stats/strided/stdevyc"
base.strided.strunc,"@stdlib/math/strided/special/strunc"
base.strided.svariance,"@stdlib/stats/strided/svariance"
base.strided.svariancech,"@stdlib/stats/strided/svariancech"
diff --git a/lib/node_modules/@stdlib/namespace/alias2pkg/data/data.json b/lib/node_modules/@stdlib/namespace/alias2pkg/data/data.json
index 671afd790efc..6437a81a326f 100644
--- a/lib/node_modules/@stdlib/namespace/alias2pkg/data/data.json
+++ b/lib/node_modules/@stdlib/namespace/alias2pkg/data/data.json
@@ -1 +1 @@
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het.kurtosis":"@stdlib/stats/base/dists/frechet/kurtosis","base.dists.frechet.logcdf":"@stdlib/stats/base/dists/frechet/logcdf","base.dists.frechet.logpdf":"@stdlib/stats/base/dists/frechet/logpdf","base.dists.frechet.mean":"@stdlib/stats/base/dists/frechet/mean","base.dists.frechet.median":"@stdlib/stats/base/dists/frechet/median","base.dists.frechet.mode":"@stdlib/stats/base/dists/frechet/mode","base.dists.frechet.pdf":"@stdlib/stats/base/dists/frechet/pdf","base.dists.frechet.quantile":"@stdlib/stats/base/dists/frechet/quantile","base.dists.frechet.skewness":"@stdlib/stats/base/dists/frechet/skewness","base.dists.frechet.stdev":"@stdlib/stats/base/dists/frechet/stdev","base.dists.frechet.variance":"@stdlib/stats/base/dists/frechet/variance","base.dists.gamma.cdf":"@stdlib/stats/base/dists/gamma/cdf","base.dists.gamma.entropy":"@stdlib/stats/base/dists/gamma/entropy","base.dists.gamma.Gamma":"@stdlib/stats/base/dists/gamma/ctor","base.dists.gamma.kurtosis":"@stdlib/stats/base/dists/gamma/kurtosis","base.dists.gamma.logcdf":"@stdlib/stats/base/dists/gamma/logcdf","base.dists.gamma.logpdf":"@stdlib/stats/base/dists/gamma/logpdf","base.dists.gamma.mean":"@stdlib/stats/base/dists/gamma/mean","base.dists.gamma.mgf":"@stdlib/stats/base/dists/gamma/mgf","base.dists.gamma.mode":"@stdlib/stats/base/dists/gamma/mode","base.dists.gamma.pdf":"@stdlib/stats/base/dists/gamma/pdf","base.dists.gamma.quantile":"@stdlib/stats/base/dists/gamma/quantile","base.dists.gamma.skewness":"@stdlib/stats/base/dists/gamma/skewness","base.dists.gamma.stdev":"@stdlib/stats/base/dists/gamma/stdev","base.dists.gamma.variance":"@stdlib/stats/base/dists/gamma/variance","base.dists.geometric.cdf":"@stdlib/stats/base/dists/geometric/cdf","base.dists.geometric.entropy":"@stdlib/stats/base/dists/geometric/entropy","base.dists.geometric.Geometric":"@stdlib/stats/base/dists/geometric/ctor","base.dists.geometric.kurtosis":"@stdlib/stats/base/dists/geometric/kurtosis","base.dists.geometric.logcdf":"@stdlib/stats/base/dists/geometric/logcdf","base.dists.geometric.logpmf":"@stdlib/stats/base/dists/geometric/logpmf","base.dists.geometric.mean":"@stdlib/stats/base/dists/geometric/mean","base.dists.geometric.median":"@stdlib/stats/base/dists/geometric/median","base.dists.geometric.mgf":"@stdlib/stats/base/dists/geometric/mgf","base.dists.geometric.mode":"@stdlib/stats/base/dists/geometric/mode","base.dists.geometric.pmf":"@stdlib/stats/base/dists/geometric/pmf","base.dists.geometric.quantile":"@stdlib/stats/base/dists/geometric/quantile","base.dists.geometric.skewness":"@stdlib/stats/base/dists/geometric/skewness","base.dists.geometric.stdev":"@stdlib/stats/base/dists/geometric/stdev","base.dists.geometric.variance":"@stdlib/stats/base/dists/geometric/variance","base.dists.gumbel.cdf":"@stdlib/stats/base/dists/gumbel/cdf","base.dists.gumbel.entropy":"@stdlib/stats/base/dists/gumbel/entropy","base.dists.gumbel.Gumbel":"@stdlib/stats/base/dists/gumbel/ctor","base.dists.gumbel.kurtosis":"@stdlib/stats/base/dists/gumbel/kurtosis","base.dists.gumbel.logcdf":"@stdlib/stats/base/dists/gumbel/logcdf","base.dists.gumbel.logpdf":"@stdlib/stats/base/dists/gumbel/logpdf","base.dists.gumbel.mean":"@stdlib/stats/base/dists/gumbel/mean","base.dists.gumbel.median":"@stdlib/stats/base/dists/gumbel/median","base.dists.gumbel.mgf":"@stdlib/stats/base/dists/gumbel/mgf","base.dists.gumbel.mode":"@stdlib/stats/base/dists/gumbel/mode","base.dists.gumbel.pdf":"@stdlib/stats/base/dists/gumbel/pdf","base.dists.gumbel.quantile":"@stdlib/stats/base/dists/gumbel/quantile","base.dists.gumbel.skewness":"@stdlib/stats/base/dists/gumbel/skewness","base.dists.gumbel.stdev":"@stdlib/stats/base/dists/gumbel/stdev","base.dists.gumbel.variance":"@stdlib/stats/base/dists/gumbel/variance","base.dists.hypergeometric.cdf":"@stdlib/stats/base/dists/hypergeometric/cdf","base.dists.hypergeometric.Hypergeometric":"@stdlib/stats/base/dists/hypergeometric/ctor","base.dists.hypergeometric.kurtosis":"@stdlib/stats/base/dists/hypergeometric/kurtosis","base.dists.hypergeometric.logpmf":"@stdlib/stats/base/dists/hypergeometric/logpmf","base.dists.hypergeometric.mean":"@stdlib/stats/base/dists/hypergeometric/mean","base.dists.hypergeometric.mode":"@stdlib/stats/base/dists/hypergeometric/mode","base.dists.hypergeometric.pmf":"@stdlib/stats/base/dists/hypergeometric/pmf","base.dists.hypergeometric.quantile":"@stdlib/stats/base/dists/hypergeometric/quantile","base.dists.hypergeometric.skewness":"@stdlib/stats/base/dists/hypergeometric/skewness","base.dists.hypergeometric.stdev":"@stdlib/stats/base/dists/hypergeometric/stdev","base.dists.hypergeometric.variance":"@stdlib/stats/base/dists/hypergeometric/variance","base.dists.invgamma.cdf":"@stdlib/stats/base/dists/invgamma/cdf","base.dists.invgamma.entropy":"@stdlib/stats/base/dists/invgamma/entropy","base.dists.invgamma.InvGamma":"@stdlib/stats/base/dists/invgamma/ctor","base.dists.invgamma.kurtosis":"@stdlib/stats/base/dists/invgamma/kurtosis","base.dists.in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/string/remove-utf8-bom","removeWords":"@stdlib/string/remove-words","rename":"@stdlib/fs/rename","reNativeFunction":"@stdlib/regexp/native-function","reorderArguments":"@stdlib/utils/reorder-arguments","repeat":"@stdlib/string/repeat","replace":"@stdlib/string/replace","replaceBefore":"@stdlib/string/replace-before","reRegExp":"@stdlib/regexp/regexp","rescape":"@stdlib/utils/escape-regexp-string","reSemVer":"@stdlib/regexp/semver","resolveParentPath":"@stdlib/fs/resolve-parent-path","resolveParentPathBy":"@stdlib/fs/resolve-parent-path-by","reUncPath":"@stdlib/regexp/unc-path","reUtf16SurrogatePair":"@stdlib/regexp/utf16-surrogate-pair","reUtf16UnpairedSurrogate":"@stdlib/regexp/utf16-unpaired-surrogate","reverseArguments":"@stdlib/utils/reverse-arguments","reverseString":"@stdlib/string/reverse","reviveBasePRNG":"@stdlib/random/base/reviver","reviveBuffer":"@stdlib/buffer/reviver","reviveComplex":"@stdlib/complex/reviver","reviveComplex64":"@stdlib/complex/float32/reviver","reviveComplex128":"@stdlib/complex/float64/reviver","reviveError":"@stdlib/error/reviver","reviveRegExp":"@stdlib/regexp/reviver","reviveTypedArray":"@stdlib/array/reviver","reWhitespace":"@stdlib/regexp/whitespace","rpad":"@stdlib/string/right-pad","rtrim":"@stdlib/string/right-trim","rtrimN":"@stdlib/string/right-trim-n","safeintmax":"@stdlib/utils/safe-int-max","safeintmin":"@stdlib/utils/safe-int-min","sample":"@stdlib/random/sample","SAVOY_STOPWORDS_FIN":"@stdlib/datasets/savoy-stopwords-fin","SAVOY_STOPWORDS_FR":"@stdlib/datasets/savoy-stopwords-fr","SAVOY_STOPWORDS_GER":"@stdlib/datasets/savoy-stopwords-ger","SAVOY_STOPWORDS_IT":"@stdlib/datasets/savoy-stopwords-it","SAVOY_STOPWORDS_POR":"@stdlib/datasets/savoy-stopwords-por","SAVOY_STOPWORDS_SP":"@stdlib/datasets/savoy-stopwords-sp","SAVOY_STOPWORDS_SWE":"@stdlib/datasets/savoy-stopwords-swe","scalar2array":"@stdlib/array/from-scalar","scalar2ndarray":"@stdlib/ndarray/from-scalar","sdot":"@stdlib/blas/sdot","SECONDS_IN_DAY":"@stdlib/constants/time/seconds-in-day","SECONDS_IN_HOUR":"@stdlib/constants/time/seconds-in-hour","SECONDS_IN_MINUTE":"@stdlib/constants/time/seconds-in-minute","SECONDS_IN_WEEK":"@stdlib/constants/time/seconds-in-week","secondsInMonth":"@stdlib/time/seconds-in-month","secondsInYear":"@stdlib/time/seconds-in-year","sentencize":"@stdlib/nlp/sentencize","seq2slice":"@stdlib/slice/seq2slice","setConfigurableReadOnly":"@stdlib/utils/define-configurable-read-only-property","setConfigurableReadOnlyAccessor":"@stdlib/utils/define-configurable-read-only-accessor","setConfigurableReadWriteAccessor":"@stdlib/utils/define-configurable-read-write-accessor","setConfigurableWriteOnlyAccessor":"@stdlib/utils/define-configurable-write-only-accessor","setMemoizedConfigurableReadOnly":"@stdlib/utils/define-memoized-configurable-read-only-property","setMemoizedReadOnly":"@stdlib/utils/define-memoized-read-only-property","setNonEnumerableProperty":"@stdlib/utils/define-nonenumerable-property","setNonEnumerableReadOnly":"@stdlib/utils/define-nonenumerable-read-only-property","setNonEnumerableReadOnlyAccessor":"@stdlib/utils/define-nonenumerable-read-only-accessor","setNonEnumerableReadWriteAccessor":"@stdlib/utils/define-nonenumerable-read-write-accessor","setNonEnumerableWriteOnlyAccessor":"@stdlib/utils/define-nonenumerable-write-only-accessor","setReadOnly":"@stdlib/utils/define-read-only-property","setReadOnlyAccessor":"@stdlib/utils/define-read-only-accessor","setReadWriteAccessor":"@stdlib/utils/define-read-write-accessor","setWriteOnlyAccessor":"@stdlib/utils/define-write-only-accessor","SharedArrayBuffer":"@stdlib/array/shared-buffer","shift":"@stdlib/utils/shift","shuffle":"@stdlib/random/shuffle","sizeOf":"@stdlib/utils/size-of","Slice":"@stdlib/slice/ctor","snakecase":"@stdlib/string/snakecase","some":"@stdlib/utils/some","someBy":"@stdlib/utils/some-by","someByAsync":"@stdlib/utils/async/some-by","someByRight":"@stdlib/utils/some-by-right","someByRightAsync":"@stdlib/utils/async/some-by-right","someInBy":"@stdlib/object/some-in-by","someOwnBy":"@stdlib/utils/some-own-by","SOTU":"@stdlib/datasets/sotu","SPACHE_REVISED":"@stdlib/datasets/spache-revised","SPAM_ASSASSIN":"@stdlib/datasets/spam-assassin","SparklineBase":"@stdlib/plot/sparklines/base/ctor","sparsearray2iterator":"@stdlib/array/to-sparse-iterator","sparsearray2iteratorRight":"@stdlib/array/to-sparse-iterator-right","splitStream":"@stdlib/streams/node/split","SQRT_EPS":"@stdlib/constants/float64/sqrt-eps","SQRT_HALF":"@stdlib/constants/float64/sqrt-half","SQRT_HALF_PI":"@stdlib/constants/float64/sqrt-half-pi","SQRT_PHI":"@stdlib/constants/float64/sqrt-phi","SQRT_PI":"@stdlib/constants/float64/sqrt-pi","SQRT_THREE":"@stdlib/constants/float64/sqrt-three","SQRT_TWO":"@stdlib/constants/float64/sqrt-two","SQRT_TWO_PI":"@stdlib/constants/float64/sqrt-two-pi","SSA_US_BIRTHS_2000_2014":"@stdlib/datasets/ssa-us-births-2000-2014","sswap":"@stdlib/blas/sswap","Stack":"@stdlib/dstructs/stack","standalone2pkg":"@stdlib/namespace/standalone2pkg","STANDARD_CARD_DECK":"@stdlib/datasets/standard-card-deck","startcase":"@stdlib/string/startcase","startsWith":"@stdlib/string/starts-with","STOPWORDS_EN":"@stdlib/datasets/stopwords-en","strided.abs":"@stdlib/math/strided/special/abs","strided.abs2":"@stdlib/math/strided/special/abs2","strided.abs2By":"@stdlib/math/strided/special/abs2-by","strided.absBy":"@stdlib/math/strided/special/abs-by","strided.acosBy":"@stdlib/math/strided/special/acos-by","strided.acoshBy":"@stdlib/math/strided/special/acosh-by","strided.acotBy":"@stdlib/math/strided/special/acot-by","strided.acothBy":"@stdlib/math/strided/special/acoth-by","strided.acovercosBy":"@stdlib/math/strided/special/acovercos-by","strided.acoversinBy":"@stdlib/math/strided/special/acoversin-by","strided.add":"@stdlib/math/strided/ops/add","strided.addBy":"@stdlib/math/strided/ops/add-by","strided.ahavercosBy":"@stdlib/math/strided/special/ahavercos-by","strided.ahaversinBy":"@stdlib/math/strided/special/ahaversin-by","strided.asinBy":"@stdlib/math/strided/special/asin-by","strided.asinhBy":"@stdlib/math/strided/special/asinh-by","strided.atanBy":"@stdlib/math/strided/special/atan-by","strided.atanhBy":"@stdlib/math/strided/special/atanh-by","strided.avercosBy":"@stdlib/math/strided/special/avercos-by","strided.aversinBy":"@stdlib/math/strided/special/aversin-by","strided.besselj0By":"@stdlib/math/strided/special/besselj0-by","strided.besselj1By":"@stdlib/math/strided/special/besselj1-by","strided.bessely0By":"@stdlib/math/strided/special/bessely0-by","strided.bessely1By":"@stdlib/math/strided/special/bessely1-by","strided.binetBy":"@stdlib/math/strided/special/binet-by","strided.cbrt":"@stdlib/math/strided/special/cbrt","strided.cbrtBy":"@stdlib/math/strided/special/cbrt-by","strided.ceil":"@stdlib/math/strided/special/ceil","strided.cosBy":"@stdlib/math/strided/special/cos-by","strided.deg2rad":"@stdlib/math/strided/special/deg2rad","strided.dataTypes":"@stdlib/strided/dtypes","strided.dcbrtBy":"@stdlib/math/strided/special/dcbrt-by","strided.dispatch":"@stdlib/strided/dispatch","strided.dispatchBy":"@stdlib/strided/dispatch-by","strided.floor":"@stdlib/math/strided/special/floor","strided.inv":"@stdlib/math/strided/special/inv","strided.mul":"@stdlib/math/strided/ops/mul","strided.mulBy":"@stdlib/math/strided/ops/mul-by","strided.ramp":"@stdlib/math/strided/special/ramp","strided.rsqrt":"@stdlib/math/strided/special/rsqrt","strided.sinBy":"@stdlib/math/strided/special/sin-by","strided.sqrt":"@stdlib/math/strided/special/sqrt","strided.sqrtBy":"@stdlib/math/strided/special/sqrt-by","strided.sub":"@stdlib/math/strided/ops/sub","strided.subBy":"@stdlib/math/strided/ops/sub-by","strided.trunc":"@stdlib/math/strided/special/trunc","stridedarray2iterator":"@stdlib/array/to-strided-iterator","stridedArrayStream":"@stdlib/streams/node/from-strided-array","string2buffer":"@stdlib/buffer/from-string","structFactory":"@stdlib/dstructs/struct","sub2ind":"@stdlib/ndarray/sub2ind","substringAfter":"@stdlib/string/substring-after","substringAfterLast":"@stdlib/string/substring-after-last","substringBefore":"@stdlib/string/substring-before","substringBeforeLast":"@stdlib/string/substring-before-last","SUTHAHARAN_MULTI_HOP_SENSOR_NETWORK":"@stdlib/datasets/suthaharan-multi-hop-sensor-network","SUTHAHARAN_SINGLE_HOP_SENSOR_NETWORK":"@stdlib/datasets/suthaharan-single-hop-sensor-network","Symbol":"@stdlib/symbol/ctor","tabulate":"@stdlib/utils/tabulate","tabulateBy":"@stdlib/utils/tabulate-by","tabulateByAsync":"@stdlib/utils/async/tabulate-by","thunk":"@stdlib/function/thunk","tic":"@stdlib/time/tic","timeit":"@stdlib/utils/timeit","tmpdir":"@stdlib/os/tmpdir","toc":"@stdlib/time/toc","tokenize":"@stdlib/nlp/tokenize","transformStream":"@stdlib/streams/node/transform","trim":"@stdlib/string/trim","truncate":"@stdlib/string/truncate","truncateMiddle":"@stdlib/string/truncate-middle","trycatch":"@stdlib/utils/try-catch","trycatchAsync":"@stdlib/utils/async/try-catch","tryFunction":"@stdlib/utils/try-function","tryRequire":"@stdlib/utils/try-require","trythen":"@stdlib/utils/try-then","trythenAsync":"@stdlib/utils/async/try-then","ttest":"@stdlib/stats/ttest","ttest2":"@stdlib/stats/ttest2","TWO_PI":"@stdlib/constants/float64/two-pi","typedarray":"@stdlib/array/typed","typedarray2json":"@stdlib/array/to-json","typedarrayCtors":"@stdlib/array/typed-ctors","typedarrayDataTypes":"@stdlib/array/typed-dtypes","typedarraypool":"@stdlib/array/pool","typemax":"@stdlib/utils/type-max","typemin":"@stdlib/utils/type-min","typeOf":"@stdlib/utils/type-of","UINT8_MAX":"@stdlib/constants/uint8/max","UINT8_NUM_BYTES":"@stdlib/constants/uint8/num-bytes","Uint8Array":"@stdlib/array/uint8","Uint8ClampedArray":"@stdlib/array/uint8c","UINT16_MAX":"@stdlib/constants/uint16/max","UINT16_NUM_BYTES":"@stdlib/constants/uint16/num-bytes","Uint16Array":"@stdlib/array/uint16","UINT32_MAX":"@stdlib/constants/uint32/max","UINT32_NUM_BYTES":"@stdlib/constants/uint32/num-bytes","Uint32Array":"@stdlib/array/uint32","umask":"@stdlib/process/umask","uncapitalize":"@stdlib/string/uncapitalize","uncapitalizeKeys":"@stdlib/utils/uncapitalize-keys","uncurry":"@stdlib/utils/uncurry","uncurryRight":"@stdlib/utils/uncurry-right","UNICODE_MAX":"@stdlib/constants/unicode/max","UNICODE_MAX_BMP":"@stdlib/constants/unicode/max-bmp","UnicodeColumnChartSparkline":"@stdlib/plot/sparklines/unicode/column","UnicodeLineChartSparkline":"@stdlib/plot/sparklines/unicode/line","UnicodeSparkline":"@stdlib/plot/sparklines/unicode","UnicodeTristateChartSparkline":"@stdlib/plot/sparklines/unicode/tristate","UnicodeUpDownChartSparkline":"@stdlib/plot/sparklines/unicode/up-down","UnicodeWinLossChartSparkline":"@stdlib/plot/sparklines/unicode/win-loss","unlink":"@stdlib/fs/unlink","unshift":"@stdlib/utils/unshift","until":"@stdlib/utils/until","untilAsync":"@stdlib/utils/async/until","untilEach":"@stdlib/utils/until-each","untilEachRight":"@stdlib/utils/until-each-right","unzip":"@stdlib/utils/unzip","uppercase":"@stdlib/string/uppercase","uppercaseKeys":"@stdlib/utils/uppercase-keys","US_STATES_ABBR":"@stdlib/datasets/us-states-abbr","US_STATES_CAPITALS":"@stdlib/datasets/us-states-capitals","US_STATES_CAPITALS_NAMES":"@stdlib/datasets/us-states-capitals-names","US_STATES_NAMES":"@stdlib/datasets/us-states-names","US_STATES_NAMES_CAPITALS":"@stdlib/datasets/us-states-names-capitals","utf16ToUTF8Array":"@stdlib/string/utf16-to-utf8-array","vartest":"@stdlib/stats/vartest","waterfall":"@stdlib/utils/async/series-waterfall","WebAssemblyMemory":"@stdlib/wasm/memory","whileAsync":"@stdlib/utils/async/while","whileEach":"@stdlib/utils/while-each","whileEachRight":"@stdlib/utils/while-each-right","whilst":"@stdlib/utils/while","wilcoxon":"@stdlib/stats/wilcoxon","writableProperties":"@stdlib/utils/writable-properties","writablePropertiesIn":"@stdlib/utils/writable-properties-in","writablePropertyNames":"@stdlib/utils/writable-property-names","writablePropertyNamesIn":"@stdlib/utils/writable-property-names-in","writablePropertySymbols":"@stdlib/utils/writable-property-symbols","writablePropertySymbolsIn":"@stdlib/utils/writable-property-symbols-in","writeFile":"@stdlib/fs/write-file","zip":"@stdlib/utils/zip","ztest":"@stdlib/stats/ztest","ztest2":"@stdlib/stats/ztest2"}
diff --git a/lib/node_modules/@stdlib/namespace/alias2standalone/data/data.csv b/lib/node_modules/@stdlib/namespace/alias2standalone/data/data.csv
index 1822a8db2d3f..a243d5e5a4b1 100644
--- a/lib/node_modules/@stdlib/namespace/alias2standalone/data/data.csv
+++ b/lib/node_modules/@stdlib/namespace/alias2standalone/data/data.csv
@@ -1430,12 +1430,12 @@ base.strided.ssumkbn2,"@stdlib/blas-ext-base-ssumkbn2"
base.strided.ssumors,"@stdlib/blas-ext-base-ssumors"
base.strided.ssumpw,"@stdlib/blas-ext-base-ssumpw"
base.strided.sswap,"@stdlib/blas-base-sswap"
-base.strided.stdev,"@stdlib/stats-base-stdev"
-base.strided.stdevch,"@stdlib/stats-base-stdevch"
-base.strided.stdevpn,"@stdlib/stats-base-stdevpn"
-base.strided.stdevtk,"@stdlib/stats-base-stdevtk"
-base.strided.stdevwd,"@stdlib/stats-base-stdevwd"
-base.strided.stdevyc,"@stdlib/stats-base-stdevyc"
+base.strided.stdev,"@stdlib/stats-strided-stdev"
+base.strided.stdevch,"@stdlib/stats-strided-stdevch"
+base.strided.stdevpn,"@stdlib/stats-strided-stdevpn"
+base.strided.stdevtk,"@stdlib/stats-strided-stdevtk"
+base.strided.stdevwd,"@stdlib/stats-strided-stdevwd"
+base.strided.stdevyc,"@stdlib/stats-strided-stdevyc"
base.strided.strunc,"@stdlib/math-strided-special-strunc"
base.strided.svariance,"@stdlib/stats-strided-svariance"
base.strided.svariancech,"@stdlib/stats-strided-svariancech"
diff --git a/lib/node_modules/@stdlib/namespace/alias2standalone/data/data.json b/lib/node_modules/@stdlib/namespace/alias2standalone/data/data.json
index dbaa133f38dc..4821a9ed242a 100644
--- a/lib/node_modules/@stdlib/namespace/alias2standalone/data/data.json
+++ b/lib/node_modules/@stdlib/namespace/alias2standalone/data/data.json
@@ -1 +1 @@
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het.kurtosis":"@stdlib/stats-base-dists-frechet-kurtosis","base.dists.frechet.logcdf":"@stdlib/stats-base-dists-frechet-logcdf","base.dists.frechet.logpdf":"@stdlib/stats-base-dists-frechet-logpdf","base.dists.frechet.mean":"@stdlib/stats-base-dists-frechet-mean","base.dists.frechet.median":"@stdlib/stats-base-dists-frechet-median","base.dists.frechet.mode":"@stdlib/stats-base-dists-frechet-mode","base.dists.frechet.pdf":"@stdlib/stats-base-dists-frechet-pdf","base.dists.frechet.quantile":"@stdlib/stats-base-dists-frechet-quantile","base.dists.frechet.skewness":"@stdlib/stats-base-dists-frechet-skewness","base.dists.frechet.stdev":"@stdlib/stats-base-dists-frechet-stdev","base.dists.frechet.variance":"@stdlib/stats-base-dists-frechet-variance","base.dists.gamma.cdf":"@stdlib/stats-base-dists-gamma-cdf","base.dists.gamma.entropy":"@stdlib/stats-base-dists-gamma-entropy","base.dists.gamma.Gamma":"@stdlib/stats-base-dists-gamma-ctor","base.dists.gamma.kurtosis":"@stdlib/stats-base-dists-gamma-kurtosis","base.dists.gamma.logcdf":"@stdlib/stats-base-dists-gamma-logcdf","base.dists.gamma.logpdf":"@stdlib/stats-base-dists-gamma-logpdf","base.dists.gamma.mean":"@stdlib/stats-base-dists-gamma-mean","base.dists.gamma.mgf":"@stdlib/stats-base-dists-gamma-mgf","base.dists.gamma.mode":"@stdlib/stats-base-dists-gamma-mode","base.dists.gamma.pdf":"@stdlib/stats-base-dists-gamma-pdf","base.dists.gamma.quantile":"@stdlib/stats-base-dists-gamma-quantile","base.dists.gamma.skewness":"@stdlib/stats-base-dists-gamma-skewness","base.dists.gamma.stdev":"@stdlib/stats-base-dists-gamma-stdev","base.dists.gamma.variance":"@stdlib/stats-base-dists-gamma-variance","base.dists.geometric.cdf":"@stdlib/stats-base-dists-geometric-cdf","base.dists.geometric.entropy":"@stdlib/stats-base-dists-geometric-entropy","base.dists.geometric.Geometric":"@stdlib/stats-base-dists-geometric-ctor","base.dists.geometric.kurtosis":"@stdlib/stats-base-dists-geometric-kurtosis","base.dists.geometric.logcdf":"@stdlib/stats-base-dists-geometric-logcdf","base.dists.geometric.logpmf":"@stdlib/stats-base-dists-geometric-logpmf","base.dists.geometric.mean":"@stdlib/stats-base-dists-geometric-mean","base.dists.geometric.median":"@stdlib/stats-base-dists-geometric-median","base.dists.geometric.mgf":"@stdlib/stats-base-dists-geometric-mgf","base.dists.geometric.mode":"@stdlib/stats-base-dists-geometric-mode","base.dists.geometric.pmf":"@stdlib/stats-base-dists-geometric-pmf","base.dists.geometric.quantile":"@stdlib/stats-base-dists-geometric-quantile","base.dists.geometric.skewness":"@stdlib/stats-base-dists-geometric-skewness","base.dists.geometric.stdev":"@stdlib/stats-base-dists-geometric-stdev","base.dists.geometric.variance":"@stdlib/stats-base-dists-geometric-variance","base.dists.gumbel.cdf":"@stdlib/stats-base-dists-gumbel-cdf","base.dists.gumbel.entropy":"@stdlib/stats-base-dists-gumbel-entropy","base.dists.gumbel.Gumbel":"@stdlib/stats-base-dists-gumbel-ctor","base.dists.gumbel.kurtosis":"@stdlib/stats-base-dists-gumbel-kurtosis","base.dists.gumbel.logcdf":"@stdlib/stats-base-dists-gumbel-logcdf","base.dists.gumbel.logpdf":"@stdlib/stats-base-dists-gumbel-logpdf","base.dists.gumbel.mean":"@stdlib/stats-base-dists-gumbel-mean","base.dists.gumbel.median":"@stdlib/stats-base-dists-gumbel-median","base.dists.gumbel.mgf":"@stdlib/stats-base-dists-gumbel-mgf","base.dists.gumbel.mode":"@stdlib/stats-base-dists-gumbel-mode","base.dists.gumbel.pdf":"@stdlib/stats-base-dists-gumbel-pdf","base.dists.gumbel.quantile":"@stdlib/stats-base-dists-gumbel-quantile","base.dists.gumbel.skewness":"@stdlib/stats-base-dists-gumbel-skewness","base.dists.gumbel.stdev":"@stdlib/stats-base-dists-gumbel-stdev","base.dists.gumbel.variance":"@stdlib/stats-base-dists-gumbel-variance","base.dists.hypergeometric.cdf":"@stdlib/stats-base-dists-hypergeometric-cdf","base.dists.hypergeometric.Hypergeometric":"@stdlib/stats-base-dists-hypergeometric-ctor","base.dists.hypergeometric.kurtosis":"@stdlib/stats-base-dists-hypergeometric-kurtosis","base.dists.hypergeometric.logpmf":"@stdlib/stats-base-dists-hypergeometric-logpmf","base.dists.hypergeometric.mean":"@stdlib/stats-base-dists-hypergeometric-mean","base.dists.hypergeometric.mode":"@stdlib/stats-base-dists-hypergeometric-mode","base.dists.hypergeometric.pmf":"@stdlib/stats-base-dists-hypergeometric-pmf","base.dists.hypergeometric.quantile":"@stdlib/stats-base-dists-hypergeometric-quantile","base.dists.hypergeometric.skewness":"@stdlib/stats-base-dists-hypergeometric-skewness","base.dists.hypergeometric.stdev":"@stdlib/stats-base-dists-hypergeometric-stdev","base.dists.hypergeometric.variance":"@stdlib/stats-base-dists-hypergeometric-variance","base.dists.invgamma.cdf":"@stdlib/stats-base-dists-invgamma-cdf","base.dists.invgamma.entropy":"@stdlib/stats-base-dists-invgamma-entropy","base.dists.invgamma.InvGamma":"@stdlib/stats-base-dists-invgamma-ctor","base.dists.invgamma.kurtosis":"@stdlib/stats-base-dists-invgamma-kurtosis","base.dists.in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ponential","random.streams.f":"@stdlib/random-streams-f","random.streams.frechet":"@stdlib/random-streams-frechet","random.streams.gamma":"@stdlib/random-streams-gamma","random.streams.geometric":"@stdlib/random-streams-geometric","random.streams.gumbel":"@stdlib/random-streams-gumbel","random.streams.hypergeometric":"@stdlib/random-streams-hypergeometric","random.streams.improvedZiggurat":"@stdlib/random-streams-improved-ziggurat","random.streams.invgamma":"@stdlib/random-streams-invgamma","random.streams.kumaraswamy":"@stdlib/random-streams-kumaraswamy","random.streams.laplace":"@stdlib/random-streams-laplace","random.streams.levy":"@stdlib/random-streams-levy","random.streams.logistic":"@stdlib/random-streams-logistic","random.streams.lognormal":"@stdlib/random-streams-lognormal","random.streams.minstd":"@stdlib/random-streams-minstd","random.streams.minstdShuffle":"@stdlib/random-streams-minstd-shuffle","random.streams.mt19937":"@stdlib/random-streams-mt19937","random.streams.negativeBinomial":"@stdlib/random-streams-negative-binomial","random.streams.normal":"@stdlib/random-streams-normal","random.streams.pareto1":"@stdlib/random-streams-pareto-type1","random.streams.poisson":"@stdlib/random-streams-poisson","random.streams.randi":"@stdlib/random-streams-randi","random.streams.randn":"@stdlib/random-streams-randn","random.streams.randu":"@stdlib/random-streams-randu","random.streams.rayleigh":"@stdlib/random-streams-rayleigh","random.streams.t":"@stdlib/random-streams-t","random.streams.triangular":"@stdlib/random-streams-triangular","random.streams.uniform":"@stdlib/random-streams-uniform","random.streams.weibull":"@stdlib/random-streams-weibull","random.strided.arcsine":"@stdlib/random-strided-arcsine","random.strided.bernoulli":"@stdlib/random-strided-bernoulli","random.strided.beta":"@stdlib/random-strided-beta","random.strided.betaprime":"@stdlib/random-strided-betaprime","random.strided.chi":"@stdlib/random-strided-chi","random.strided.chisquare":"@stdlib/random-strided-chisquare","random.strided.cosine":"@stdlib/random-strided-cosine","random.strided.discreteUniform":"@stdlib/random-strided-discrete-uniform","random.strided.exponential":"@stdlib/random-strided-exponential","random.strided.gamma":"@stdlib/random-strided-gamma","random.strided.geometric":"@stdlib/random-strided-geometric","random.strided.invgamma":"@stdlib/random-strided-invgamma","random.strided.lognormal":"@stdlib/random-strided-lognormal","random.strided.minstd":"@stdlib/random-strided-minstd","random.strided.minstdShuffle":"@stdlib/random-strided-minstd-shuffle","random.strided.mt19937":"@stdlib/random-strided-mt19937","random.strided.normal":"@stdlib/random-strided-normal","random.strided.poisson":"@stdlib/random-strided-poisson","random.strided.randu":"@stdlib/random-strided-randu","random.strided.rayleigh":"@stdlib/random-strided-rayleigh","random.strided.t":"@stdlib/random-strided-t","random.strided.uniform":"@stdlib/random-strided-uniform","random.strided.weibull":"@stdlib/random-strided-weibull","ranks":"@stdlib/stats-ranks","readDir":"@stdlib/fs-read-dir","readFile":"@stdlib/fs-read-file","readFileList":"@stdlib/fs-read-file-list","readJSON":"@stdlib/fs-read-json","readWASM":"@stdlib/fs-read-wasm","real":"@stdlib/complex-float64-real","realarray":"@stdlib/array-typed-real","realarrayCtors":"@stdlib/array-typed-real-ctors","realarrayDataTypes":"@stdlib/array-typed-real-dtypes","realf":"@stdlib/complex-float32-real","realmax":"@stdlib/utils-real-max","realmin":"@stdlib/utils-real-min","reBasename":"@stdlib/regexp-basename","reBasenamePosix":"@stdlib/regexp-basename-posix","reBasenameWindows":"@stdlib/regexp-basename-windows","reColorHexadecimal":"@stdlib/regexp-color-hexadecimal","reDecimalNumber":"@stdlib/regexp-decimal-number","reDirname":"@stdlib/regexp-dirname","reDirnamePosix":"@stdlib/regexp-dirname-posix","reDirnameWindows":"@stdlib/regexp-dirname-windows","reduce":"@stdlib/utils-reduce","reduce2d":"@stdlib/utils-reduce2d","reduceAsync":"@stdlib/utils-async-reduce","reduceRight":"@stdlib/utils-reduce-right","reduceRightAsync":"@stdlib/utils-async-reduce-right","reDurationString":"@stdlib/regexp-duration-string","reEOL":"@stdlib/regexp-eol","reExtendedLengthPath":"@stdlib/regexp-extended-length-path","reExtname":"@stdlib/regexp-extname","reExtnamePosix":"@stdlib/regexp-extname-posix","reExtnameWindows":"@stdlib/regexp-extname-windows","reFilename":"@stdlib/regexp-filename","reFilenamePosix":"@stdlib/regexp-filename-posix","reFilenameWindows":"@stdlib/regexp-filename-windows","reFromString":"@stdlib/utils-regexp-from-string","reFunctionName":"@stdlib/regexp-function-name","regexp2json":"@stdlib/regexp-to-json","reim":"@stdlib/complex-float64-reim","reimf":"@stdlib/complex-float32-reim","rejectArguments":"@stdlib/utils-reject-arguments","removeFirst":"@stdlib/string-remove-first","removeLast":"@stdlib/string-remove-last","removePunctuation":"@stdlib/string-remove-punctuation","removeUTF8BOM":"@stdlib/string-remove-utf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","base.cmulf":"@stdlib/complex-float32-base-mul","base.cneg":"@stdlib/complex-float64-base-neg","base.cnegf":"@stdlib/complex-float32-base-neg","base.codePointAt":"@stdlib/string-base-code-point-at","base.constantcase":"@stdlib/string-base-constantcase","base.continuedFraction":"@stdlib/math-base-tools-continued-fraction","base.copysign":"@stdlib/math-base-special-copysign","base.copysignf":"@stdlib/math-base-special-copysignf","base.cos":"@stdlib/math-base-special-cos","base.cosd":"@stdlib/math-base-special-cosd","base.cosh":"@stdlib/math-base-special-cosh","base.cosm1":"@stdlib/math-base-special-cosm1","base.cospi":"@stdlib/math-base-special-cospi","base.cot":"@stdlib/math-base-special-cot","base.cotd":"@stdlib/math-base-special-cotd","base.coth":"@stdlib/math-base-special-coth","base.covercos":"@stdlib/math-base-special-covercos","base.coversin":"@stdlib/math-base-special-coversin","base.cphase":"@stdlib/math-base-special-cphase","base.cpolar":"@stdlib/math-base-special-cpolar","base.cround":"@stdlib/math-base-special-cround","base.croundn":"@stdlib/math-base-special-croundn","base.csc":"@stdlib/math-base-special-csc","base.cscd":"@stdlib/math-base-special-cscd","base.csch":"@stdlib/math-base-special-csch","base.csignum":"@stdlib/math-base-special-csignum","base.csub":"@stdlib/complex-float64-base-sub","base.csubf":"@stdlib/complex-float32-base-sub","base.deg2rad":"@stdlib/math-base-special-deg2rad","base.deg2radf":"@stdlib/math-base-special-deg2radf","base.digamma":"@stdlib/math-base-special-digamma","base.diracDelta":"@stdlib/math-base-special-dirac-delta","base.div":"@stdlib/number-float64-base-div","base.divf":"@stdlib/number-float32-base-div","base.dotcase":"@stdlib/string-base-dotcase","base.dists.arcsine.Arcsine":"@stdlib/stats-base-dists-arcsine-ctor","base.dists.arcsine.cdf":"@stdlib/stats-base-dists-arcsine-cdf","base.dists.arcsine.entropy":"@stdlib/stats-base-dists-arcsine-entropy","base.dists.arcsine.kurtosis":"@stdlib/stats-base-dists-arcsine-kurtosis","base.dists.arcsine.logcdf":"@stdlib/stats-base-dists-arcsine-logcdf","base.dists.arcsine.logpdf":"@stdlib/stats-base-dists-arcsine-logpdf","base.dists.arcsine.mean":"@stdlib/stats-base-dists-arcsine-mean","base.dists.arcsine.median":"@stdlib/stats-base-dists-arcsine-median","base.dists.arcsine.mode":"@stdlib/stats-base-dists-arcsine-mode","base.dists.arcsine.pdf":"@stdlib/stats-base-dists-arcsine-pdf","base.dists.arcsine.quantile":"@stdlib/stats-base-dists-arcsine-quantile","base.dists.arcsine.skewness":"@stdlib/stats-base-dists-arcsine-skewness","base.dists.arcsine.stdev":"@stdlib/stats-base-dists-arcsine-stdev","base.dists.arcsine.variance":"@stdlib/stats-base-dists-arcsine-variance","base.dists.bernoulli.Bernoulli":"@stdlib/stats-base-dists-bernoulli-ctor","base.dists.bernoulli.cdf":"@stdlib/stats-base-dists-bernoulli-cdf","base.dists.bernoulli.entropy":"@stdlib/stats-base-dists-bernoulli-entropy","base.dists.bernoulli.kurtosis":"@stdlib/stats-base-dists-bernoulli-kurtosis","base.dists.bernoulli.mean":"@stdlib/stats-base-dists-bernoulli-mean","base.dists.bernoulli.median":"@stdlib/stats-base-dists-bernoulli-median","base.dists.bernoulli.mgf":"@stdlib/stats-base-dists-bernoulli-mgf","base.dists.bernoulli.mode":"@stdlib/stats-base-dists-bernoulli-mode","base.dists.bernoulli.pmf":"@stdlib/stats-base-dists-bernoulli-pmf","base.dists.bernoulli.quantile":"@stdlib/stats-base-dists-bernoulli-quantile","base.dists.bernoulli.skewness":"@stdlib/stats-base-dists-bernoulli-skewness","base.dists.bernoulli.stdev":"@stdlib/stats-base-dists-bernoulli-stdev","base.dists.bernoulli.variance":"@stdlib/stats-base-dists-bernoulli-variance","base.dists.beta.Beta":"@stdlib/stats-base-dists-beta-ctor","base.dists.beta.cdf":"@stdlib/stats-base-dists-beta-cdf","base.dists.beta.entropy":"@stdlib/stats-base-dists-beta-entropy","base.dists.beta.kurtosis":"@stdlib/stats-base-dists-beta-kurtosis","base.dists.beta.logcdf":"@stdlib/stats-base-dists-beta-logcdf","base.dists.beta.logpdf":"@stdlib/stats-base-dists-beta-logpdf","base.dists.beta.mean":"@stdlib/stats-base-dists-beta-mean","base.dists.beta.median":"@stdlib/stats-base-dists-beta-median","base.dists.beta.mgf":"@stdlib/stats-base-dists-beta-mgf","base.dists.beta.mode":"@stdlib/stats-base-dists-beta-mode","base.dists.beta.pdf":"@stdlib/stats-base-dists-beta-pdf","base.dists.beta.quantile":"@stdlib/stats-base-dists-beta-quantile","base.dists.beta.skewness":"@stdlib/stats-base-dists-beta-skewness","base.dists.beta.stdev":"@stdlib/stats-base-dists-beta-stdev","base.dists.beta.variance":"@stdlib/stats-base-dists-beta-variance","base.dists.betaprime.BetaPrime":"@stdlib/stats-base-dists-betaprime-ctor","base.dists.betaprime.cdf":"@stdlib/stats-base-dists-betaprime-cdf","base.dists.betaprime.kurtosis":"@stdlib/stats-base-dists-betaprime-kurtosis","base.dists.betaprime.logcdf":"@stdlib/stats-base-dists-betaprime-logcdf","base.dists.betaprime.logpdf":"@stdlib/stats-base-dists-betaprime-logpdf","base.dists.betaprime.mean":"@st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as-ext-base-sdssumpw","base.strided.sfill":"@stdlib/blas-ext-base-sfill","base.strided.sfloor":"@stdlib/math-strided-special-sfloor","base.strided.sinv":"@stdlib/math-strided-special-sinv","base.strided.smap":"@stdlib/strided-base-smap","base.strided.smap2":"@stdlib/strided-base-smap2","base.strided.smax":"@stdlib/stats-strided-smax","base.strided.smaxabs":"@stdlib/stats-strided-smaxabs","base.strided.smaxabssorted":"@stdlib/stats-strided-smaxabssorted","base.strided.smaxsorted":"@stdlib/stats-strided-smaxsorted","base.strided.smean":"@stdlib/stats-strided-smean","base.strided.smeankbn":"@stdlib/stats-strided-smeankbn","base.strided.smeankbn2":"@stdlib/stats-strided-smeankbn2","base.strided.smeanli":"@stdlib/stats-strided-smeanli","base.strided.smeanlipw":"@stdlib/stats-strided-smeanlipw","base.strided.smeanors":"@stdlib/stats-strided-smeanors","base.strided.smeanpn":"@stdlib/stats-strided-smeanpn","base.strided.smeanpw":"@stdlib/stats-strided-smeanpw","base.strided.smeanwd":"@stdlib/stats-strided-smeanwd","base.strided.smediansorted":"@stdlib/stats-strided-smediansorted","base.strided.smidrange":"@stdlib/stats-strided-smidrange","base.strided.smin":"@stdlib/stats-strided-smin","base.strided.sminabs":"@stdlib/stats-strided-sminabs","base.strided.sminsorted":"@stdlib/stats-strided-sminsorted","base.strided.smskabs":"@stdlib/math-strided-special-smskabs","base.strided.smskabs2":"@stdlib/math-strided-special-smskabs2","base.strided.smskcbrt":"@stdlib/math-strided-special-smskcbrt","base.strided.smskceil":"@stdlib/math-strided-special-smskceil","base.strided.smskdeg2rad":"@stdlib/math-strided-special-smskdeg2rad","base.strided.smskfloor":"@stdlib/math-strided-special-smskfloor","base.strided.smskinv":"@stdlib/math-strided-special-smskinv","base.strided.smskmap":"@stdlib/strided-base-smskmap","base.strided.smskmap2":"@stdlib/strided-base-smskmap2","base.strided.smskmax":"@stdlib/stats-strided-smskmax","base.strided.smskmin":"@stdlib/stats-strided-smskmin","base.strided.smskramp":"@stdlib/math-strided-special-smskramp","base.strided.smskrange":"@stdlib/stats-strided-smskrange","base.strided.smskrsqrt":"@stdlib/math-strided-special-smskrsqrt","base.strided.smsksqrt":"@stdlib/math-strided-special-smsksqrt","base.strided.smsktrunc":"@stdlib/math-strided-special-smsktrunc","base.strided.snanmax":"@stdlib/stats-strided-snanmax","base.strided.snanmaxabs":"@stdlib/stats-strided-snanmaxabs","base.strided.snanmean":"@stdlib/stats-base-snanmean","base.strided.snanmeanors":"@stdlib/stats-strided-snanmeanors","base.strided.snanmeanpn":"@stdlib/stats-strided-snanmeanpn","base.strided.snanmeanwd":"@stdlib/stats-strided-snanmeanwd","base.strided.snanmin":"@stdlib/stats-strided-snanmin","base.strided.snanminabs":"@stdlib/stats-strided-snanminabs","base.strided.snanmskmax":"@stdlib/stats-strided-snanmskmax","base.strided.snanmskmin":"@stdlib/stats-strided-snanmskmin","base.strided.snanmskrange":"@stdlib/stats-strided-snanmskrange","base.strided.snanrange":"@stdlib/stats-strided-snanrange","base.strided.snanstdev":"@stdlib/stats-base-snanstdev","base.strided.snanstdevch":"@stdlib/stats-base-snanstdevch","base.strided.snanstdevpn":"@stdlib/stats-base-snanstdevpn","base.strided.snanstdevtk":"@stdlib/stats-base-snanstdevtk","base.strided.snanstdevwd":"@stdlib/stats-base-snanstdevwd","base.strided.snanstdevyc":"@stdlib/stats-base-snanstdevyc","base.strided.snansum":"@stdlib/blas-ext-base-snansum","base.strided.snansumkbn":"@stdlib/blas-ext-base-snansumkbn","base.strided.snansumkbn2":"@stdlib/blas-ext-base-snansumkbn2","base.strided.snansumors":"@stdlib/blas-ext-base-snansumors","base.strided.snansumpw":"@stdlib/blas-ext-base-snansumpw","base.strided.snanvariance":"@stdlib/stats-base-snanvariance","base.strided.snanvariancech":"@stdlib/stats-base-snanvariancech","base.strided.snanvariancepn":"@stdlib/stats-base-snanvariancepn","base.strided.snanvariancetk":"@stdlib/stats-base-snanvariancetk","base.strided.snanvariancewd":"@stdlib/stats-base-snanvariancewd","base.strided.snanvarianceyc":"@stdlib/stats-base-snanvarianceyc","base.strided.snrm2":"@stdlib/blas-base-snrm2","base.strided.sramp":"@stdlib/math-strided-special-sramp","base.strided.srange":"@stdlib/stats-strided-srange","base.strided.srev":"@stdlib/blas-ext-base-srev","base.strided.srsqrt":"@stdlib/math-strided-special-srsqrt","base.strided.sscal":"@stdlib/blas-base-sscal","base.strided.ssort2hp":"@stdlib/blas-ext-base-ssort2hp","base.strided.ssort2ins":"@stdlib/blas-ext-base-ssort2ins","base.strided.ssort2sh":"@stdlib/blas-ext-base-ssort2sh","base.strided.ssorthp":"@stdlib/blas-ext-base-ssorthp","base.strided.ssortins":"@stdlib/blas-ext-base-ssortins","base.strided.ssortsh":"@stdlib/blas-ext-base-ssortsh","base.strided.ssqrt":"@stdlib/math-strided-special-ssqrt","base.strided.sstdev":"@stdlib/stats-strided-sstdev","base.strided.sstdevch":"@stdlib/stats-strided-sstdevch","base.strided.sstdevpn":"@stdlib/stats-strided-sstdevpn","base.strided.sstdevtk":"@stdlib/stats-strided-sstdev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/string-remove-utf8-bom","removeWords":"@stdlib/string-remove-words","rename":"@stdlib/fs-rename","reNativeFunction":"@stdlib/regexp-native-function","reorderArguments":"@stdlib/utils-reorder-arguments","repeat":"@stdlib/string-repeat","replace":"@stdlib/string-replace","replaceBefore":"@stdlib/string-replace-before","reRegExp":"@stdlib/regexp-regexp","rescape":"@stdlib/utils-escape-regexp-string","reSemVer":"@stdlib/regexp-semver","resolveParentPath":"@stdlib/fs-resolve-parent-path","resolveParentPathBy":"@stdlib/fs-resolve-parent-path-by","reUncPath":"@stdlib/regexp-unc-path","reUtf16SurrogatePair":"@stdlib/regexp-utf16-surrogate-pair","reUtf16UnpairedSurrogate":"@stdlib/regexp-utf16-unpaired-surrogate","reverseArguments":"@stdlib/utils-reverse-arguments","reverseString":"@stdlib/string-reverse","reviveBasePRNG":"@stdlib/random-base-reviver","reviveBuffer":"@stdlib/buffer-reviver","reviveComplex":"@stdlib/complex-reviver","reviveComplex64":"@stdlib/complex-float32-reviver","reviveComplex128":"@stdlib/complex-float64-reviver","reviveError":"@stdlib/error-reviver","reviveRegExp":"@stdlib/regexp-reviver","reviveTypedArray":"@stdlib/array-reviver","reWhitespace":"@stdlib/regexp-whitespace","rpad":"@stdlib/string-right-pad","rtrim":"@stdlib/string-right-trim","rtrimN":"@stdlib/string-right-trim-n","safeintmax":"@stdlib/utils-safe-int-max","safeintmin":"@stdlib/utils-safe-int-min","sample":"@stdlib/random-sample","SAVOY_STOPWORDS_FIN":"@stdlib/datasets-savoy-stopwords-fin","SAVOY_STOPWORDS_FR":"@stdlib/datasets-savoy-stopwords-fr","SAVOY_STOPWORDS_GER":"@stdlib/datasets-savoy-stopwords-ger","SAVOY_STOPWORDS_IT":"@stdlib/datasets-savoy-stopwords-it","SAVOY_STOPWORDS_POR":"@stdlib/datasets-savoy-stopwords-por","SAVOY_STOPWORDS_SP":"@stdlib/datasets-savoy-stopwords-sp","SAVOY_STOPWORDS_SWE":"@stdlib/datasets-savoy-stopwords-swe","scalar2array":"@stdlib/array-from-scalar","scalar2ndarray":"@stdlib/ndarray-from-scalar","sdot":"@stdlib/blas-sdot","SECONDS_IN_DAY":"@stdlib/constants-time-seconds-in-day","SECONDS_IN_HOUR":"@stdlib/constants-time-seconds-in-hour","SECONDS_IN_MINUTE":"@stdlib/constants-time-seconds-in-minute","SECONDS_IN_WEEK":"@stdlib/constants-time-seconds-in-week","secondsInMonth":"@stdlib/time-seconds-in-month","secondsInYear":"@stdlib/time-seconds-in-year","sentencize":"@stdlib/nlp-sentencize","seq2slice":"@stdlib/slice-seq2slice","setConfigurableReadOnly":"@stdlib/utils-define-configurable-read-only-property","setConfigurableReadOnlyAccessor":"@stdlib/utils-define-configurable-read-only-accessor","setConfigurableReadWriteAccessor":"@stdlib/utils-define-configurable-read-write-accessor","setConfigurableWriteOnlyAccessor":"@stdlib/utils-define-configurable-write-only-accessor","setMemoizedConfigurableReadOnly":"@stdlib/utils-define-memoized-configurable-read-only-property","setMemoizedReadOnly":"@stdlib/utils-define-memoized-read-only-property","setNonEnumerableProperty":"@stdlib/utils-define-nonenumerable-property","setNonEnumerableReadOnly":"@stdlib/utils-define-nonenumerable-read-only-property","setNonEnumerableReadOnlyAccessor":"@stdlib/utils-define-nonenumerable-read-only-accessor","setNonEnumerableReadWriteAccessor":"@stdlib/utils-define-nonenumerable-read-write-accessor","setNonEnumerableWriteOnlyAccessor":"@stdlib/utils-define-nonenumerable-write-only-accessor","setReadOnly":"@stdlib/utils-define-read-only-property","setReadOnlyAccessor":"@stdlib/utils-define-read-only-accessor","setReadWriteAccessor":"@stdlib/utils-define-read-write-accessor","setWriteOnlyAccessor":"@stdlib/utils-define-write-only-accessor","SharedArrayBuffer":"@stdlib/array-shared-buffer","shift":"@stdlib/utils-shift","shuffle":"@stdlib/random-shuffle","sizeOf":"@stdlib/utils-size-of","Slice":"@stdlib/slice-ctor","snakecase":"@stdlib/string-snakecase","some":"@stdlib/utils-some","someBy":"@stdlib/utils-some-by","someByAsync":"@stdlib/utils-async-some-by","someByRight":"@stdlib/utils-some-by-right","someByRightAsync":"@stdlib/utils-async-some-by-right","someInBy":"@stdlib/object-some-in-by","someOwnBy":"@stdlib/utils-some-own-by","SOTU":"@stdlib/datasets-sotu","SPACHE_REVISED":"@stdlib/datasets-spache-revised","SPAM_ASSASSIN":"@stdlib/datasets-spam-assassin","SparklineBase":"@stdlib/plot-sparklines-base-ctor","sparsearray2iterator":"@stdlib/array-to-sparse-iterator","sparsearray2iteratorRight":"@stdlib/array-to-sparse-iterator-right","splitStream":"@stdlib/streams-node-split","SQRT_EPS":"@stdlib/constants-float64-sqrt-eps","SQRT_HALF":"@stdlib/constants-float64-sqrt-half","SQRT_HALF_PI":"@stdlib/constants-float64-sqrt-half-pi","SQRT_PHI":"@stdlib/constants-float64-sqrt-phi","SQRT_PI":"@stdlib/constants-float64-sqrt-pi","SQRT_THREE":"@stdlib/constants-float64-sqrt-three","SQRT_TWO":"@stdlib/constants-float64-sqrt-two","SQRT_TWO_PI":"@stdlib/constants-float64-sqrt-two-pi","SSA_US_BIRTHS_2000_2014":"@stdlib/datasets-ssa-us-births-2000-2014","sswap":"@stdlib/blas-sswap","Stack":"@stdlib/dstructs-stack","standalone2pkg":"@stdlib/namespace-standalone2pkg","STANDARD_CARD_DECK":"@stdlib/datasets-standard-card-deck","startcase":"@stdlib/string-startcase","startsWith":"@stdlib/string-starts-with","STOPWORDS_EN":"@stdlib/datasets-stopwords-en","strided.abs":"@stdlib/math-strided-special-abs","strided.abs2":"@stdlib/math-strided-special-abs2","strided.abs2By":"@stdlib/math-strided-special-abs2-by","strided.absBy":"@stdlib/math-strided-special-abs-by","strided.acosBy":"@stdlib/math-strided-special-acos-by","strided.acoshBy":"@stdlib/math-strided-special-acosh-by","strided.acotBy":"@stdlib/math-strided-special-acot-by","strided.acothBy":"@stdlib/math-strided-special-acoth-by","strided.acovercosBy":"@stdlib/math-strided-special-acovercos-by","strided.acoversinBy":"@stdlib/math-strided-special-acoversin-by","strided.add":"@stdlib/math-strided-ops-add","strided.addBy":"@stdlib/math-strided-ops-add-by","strided.ahavercosBy":"@stdlib/math-strided-special-ahavercos-by","strided.ahaversinBy":"@stdlib/math-strided-special-ahaversin-by","strided.asinBy":"@stdlib/math-strided-special-asin-by","strided.asinhBy":"@stdlib/math-strided-special-asinh-by","strided.atanBy":"@stdlib/math-strided-special-atan-by","strided.atanhBy":"@stdlib/math-strided-special-atanh-by","strided.avercosBy":"@stdlib/math-strided-special-avercos-by","strided.aversinBy":"@stdlib/math-strided-special-aversin-by","strided.besselj0By":"@stdlib/math-strided-special-besselj0-by","strided.besselj1By":"@stdlib/math-strided-special-besselj1-by","strided.bessely0By":"@stdlib/math-strided-special-bessely0-by","strided.bessely1By":"@stdlib/math-strided-special-bessely1-by","strided.binetBy":"@stdlib/math-strided-special-binet-by","strided.cbrt":"@stdlib/math-strided-special-cbrt","strided.cbrtBy":"@stdlib/math-strided-special-cbrt-by","strided.ceil":"@stdlib/math-strided-special-ceil","strided.cosBy":"@stdlib/math-strided-special-cos-by","strided.deg2rad":"@stdlib/math-strided-special-deg2rad","strided.dataTypes":"@stdlib/strided-dtypes","strided.dcbrtBy":"@stdlib/math-strided-special-dcbrt-by","strided.dispatch":"@stdlib/strided-dispatch","strided.dispatchBy":"@stdlib/strided-dispatch-by","strided.floor":"@stdlib/math-strided-special-floor","strided.inv":"@stdlib/math-strided-special-inv","strided.mul":"@stdlib/math-strided-ops-mul","strided.mulBy":"@stdlib/math-strided-ops-mul-by","strided.ramp":"@stdlib/math-strided-special-ramp","strided.rsqrt":"@stdlib/math-strided-special-rsqrt","strided.sinBy":"@stdlib/math-strided-special-sin-by","strided.sqrt":"@stdlib/math-strided-special-sqrt","strided.sqrtBy":"@stdlib/math-strided-special-sqrt-by","strided.sub":"@stdlib/math-strided-ops-sub","strided.subBy":"@stdlib/math-strided-ops-sub-by","strided.trunc":"@stdlib/math-strided-special-trunc","stridedarray2iterator":"@stdlib/array-to-strided-iterator","stridedArrayStream":"@stdlib/streams-node-from-strided-array","string2buffer":"@stdlib/buffer-from-string","structFactory":"@stdlib/dstructs-struct","sub2ind":"@stdlib/ndarray-sub2ind","substringAfter":"@stdlib/string-substring-after","substringAfterLast":"@stdlib/string-substring-after-last","substringBefore":"@stdlib/string-substring-before","substringBeforeLast":"@stdlib/string-substring-before-last","SUTHAHARAN_MULTI_HOP_SENSOR_NETWORK":"@stdlib/datasets-suthaharan-multi-hop-sensor-network","SUTHAHARAN_SINGLE_HOP_SENSOR_NETWORK":"@stdlib/datasets-suthaharan-single-hop-sensor-network","Symbol":"@stdlib/symbol-ctor","tabulate":"@stdlib/utils-tabulate","tabulateBy":"@stdlib/utils-tabulate-by","tabulateByAsync":"@stdlib/utils-async-tabulate-by","thunk":"@stdlib/function-thunk","tic":"@stdlib/time-tic","timeit":"@stdlib/utils-timeit","tmpdir":"@stdlib/os-tmpdir","toc":"@stdlib/time-toc","tokenize":"@stdlib/nlp-tokenize","transformStream":"@stdlib/streams-node-transform","trim":"@stdlib/string-trim","truncate":"@stdlib/string-truncate","truncateMiddle":"@stdlib/string-truncate-middle","trycatch":"@stdlib/utils-try-catch","trycatchAsync":"@stdlib/utils-async-try-catch","tryFunction":"@stdlib/utils-try-function","tryRequire":"@stdlib/utils-try-require","trythen":"@stdlib/utils-try-then","trythenAsync":"@stdlib/utils-async-try-then","ttest":"@stdlib/stats-ttest","ttest2":"@stdlib/stats-ttest2","TWO_PI":"@stdlib/constants-float64-two-pi","typedarray":"@stdlib/array-typed","typedarray2json":"@stdlib/array-to-json","typedarrayCtors":"@stdlib/array-typed-ctors","typedarrayDataTypes":"@stdlib/array-typed-dtypes","typedarraypool":"@stdlib/array-pool","typemax":"@stdlib/utils-type-max","typemin":"@stdlib/utils-type-min","typeOf":"@stdlib/utils-type-of","UINT8_MAX":"@stdlib/constants-uint8-max","UINT8_NUM_BYTES":"@stdlib/constants-uint8-num-bytes","Uint8Array":"@stdlib/array-uint8","Uint8ClampedArray":"@stdlib/array-uint8c","UINT16_MAX":"@stdlib/constants-uint16-max","UINT16_NUM_BYTES":"@stdlib/constants-uint16-num-bytes","Uint16Array":"@stdlib/array-uint16","UINT32_MAX":"@stdlib/constants-uint32-max","UINT32_NUM_BYTES":"@stdlib/constants-uint32-num-bytes","Uint32Array":"@stdlib/array-uint32","umask":"@stdlib/process-umask","uncapitalize":"@stdlib/string-uncapitalize","uncapitalizeKeys":"@stdlib/utils-uncapitalize-keys","uncurry":"@stdlib/utils-uncurry","uncurryRight":"@stdlib/utils-uncurry-right","UNICODE_MAX":"@stdlib/constants-unicode-max","UNICODE_MAX_BMP":"@stdlib/constants-unicode-max-bmp","UnicodeColumnChartSparkline":"@stdlib/plot-sparklines-unicode-column","UnicodeLineChartSparkline":"@stdlib/plot-sparklines-unicode-line","UnicodeSparkline":"@stdlib/plot-sparklines-unicode","UnicodeTristateChartSparkline":"@stdlib/plot-sparklines-unicode-tristate","UnicodeUpDownChartSparkline":"@stdlib/plot-sparklines-unicode-up-down","UnicodeWinLossChartSparkline":"@stdlib/plot-sparklines-unicode-win-loss","unlink":"@stdlib/fs-unlink","unshift":"@stdlib/utils-unshift","until":"@stdlib/utils-until","untilAsync":"@stdlib/utils-async-until","untilEach":"@stdlib/utils-until-each","untilEachRight":"@stdlib/utils-until-each-right","unzip":"@stdlib/utils-unzip","uppercase":"@stdlib/string-uppercase","uppercaseKeys":"@stdlib/utils-uppercase-keys","US_STATES_ABBR":"@stdlib/datasets-us-states-abbr","US_STATES_CAPITALS":"@stdlib/datasets-us-states-capitals","US_STATES_CAPITALS_NAMES":"@stdlib/datasets-us-states-capitals-names","US_STATES_NAMES":"@stdlib/datasets-us-states-names","US_STATES_NAMES_CAPITALS":"@stdlib/datasets-us-states-names-capitals","utf16ToUTF8Array":"@stdlib/string-utf16-to-utf8-array","vartest":"@stdlib/stats-vartest","waterfall":"@stdlib/utils-async-series-waterfall","WebAssemblyMemory":"@stdlib/wasm-memory","whileAsync":"@stdlib/utils-async-while","whileEach":"@stdlib/utils-while-each","whileEachRight":"@stdlib/utils-while-each-right","whilst":"@stdlib/utils-while","wilcoxon":"@stdlib/stats-wilcoxon","writableProperties":"@stdlib/utils-writable-properties","writablePropertiesIn":"@stdlib/utils-writable-properties-in","writablePropertyNames":"@stdlib/utils-writable-property-names","writablePropertyNamesIn":"@stdlib/utils-writable-property-names-in","writablePropertySymbols":"@stdlib/utils-writable-property-symbols","writablePropertySymbolsIn":"@stdlib/utils-writable-property-symbols-in","writeFile":"@stdlib/fs-write-file","zip":"@stdlib/utils-zip","ztest":"@stdlib/stats-ztest","ztest2":"@stdlib/stats-ztest2"}
diff --git a/lib/node_modules/@stdlib/namespace/pkg2alias/data/data.csv b/lib/node_modules/@stdlib/namespace/pkg2alias/data/data.csv
index 53500aa04d4f..7afe0342be0c 100644
--- a/lib/node_modules/@stdlib/namespace/pkg2alias/data/data.csv
+++ b/lib/node_modules/@stdlib/namespace/pkg2alias/data/data.csv
@@ -1430,12 +1430,12 @@
"@stdlib/blas/ext/base/ssumors",base.strided.ssumors
"@stdlib/blas/ext/base/ssumpw",base.strided.ssumpw
"@stdlib/blas/base/sswap",base.strided.sswap
-"@stdlib/stats/base/stdev",base.strided.stdev
-"@stdlib/stats/base/stdevch",base.strided.stdevch
-"@stdlib/stats/base/stdevpn",base.strided.stdevpn
-"@stdlib/stats/base/stdevtk",base.strided.stdevtk
-"@stdlib/stats/base/stdevwd",base.strided.stdevwd
-"@stdlib/stats/base/stdevyc",base.strided.stdevyc
+"@stdlib/stats/strided/stdev",base.strided.stdev
+"@stdlib/stats/strided/stdevch",base.strided.stdevch
+"@stdlib/stats/strided/stdevpn",base.strided.stdevpn
+"@stdlib/stats/strided/stdevtk",base.strided.stdevtk
+"@stdlib/stats/strided/stdevwd",base.strided.stdevwd
+"@stdlib/stats/strided/stdevyc",base.strided.stdevyc
"@stdlib/math/strided/special/strunc",base.strided.strunc
"@stdlib/stats/strided/svariance",base.strided.svariance
"@stdlib/stats/strided/svariancech",base.strided.svariancech
diff --git a/lib/node_modules/@stdlib/namespace/pkg2alias/data/data.json b/lib/node_modules/@stdlib/namespace/pkg2alias/data/data.json
index 2a10b8b3f441..9a0538128ae2 100644
--- a/lib/node_modules/@stdlib/namespace/pkg2alias/data/data.json
+++ b/lib/node_modules/@stdlib/namespace/pkg2alias/data/data.json
@@ -1 +1 @@
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ponential","@stdlib/random/streams/f":"random.streams.f","@stdlib/random/streams/frechet":"random.streams.frechet","@stdlib/random/streams/gamma":"random.streams.gamma","@stdlib/random/streams/geometric":"random.streams.geometric","@stdlib/random/streams/gumbel":"random.streams.gumbel","@stdlib/random/streams/hypergeometric":"random.streams.hypergeometric","@stdlib/random/streams/improved-ziggurat":"random.streams.improvedZiggurat","@stdlib/random/streams/invgamma":"random.streams.invgamma","@stdlib/random/streams/kumaraswamy":"random.streams.kumaraswamy","@stdlib/random/streams/laplace":"random.streams.laplace","@stdlib/random/streams/levy":"random.streams.levy","@stdlib/random/streams/logistic":"random.streams.logistic","@stdlib/random/streams/lognormal":"random.streams.lognormal","@stdlib/random/streams/minstd":"random.streams.minstd","@stdlib/random/streams/minstd-shuffle":"random.streams.minstdShuffle","@stdlib/random/streams/mt19937":"random.streams.mt19937","@stdlib/random/streams/negative-binomial":"random.streams.negativeBinomial","@stdlib/random/streams/normal":"random.streams.normal","@stdlib/random/streams/pareto-type1":"random.streams.pareto1","@stdlib/random/streams/poisson":"random.streams.poisson","@stdlib/random/streams/randi":"random.streams.randi","@stdlib/random/streams/randn":"random.streams.randn","@stdlib/random/streams/randu":"random.streams.randu","@stdlib/random/streams/rayleigh":"random.streams.rayleigh","@stdlib/random/streams/t":"random.streams.t","@stdlib/random/streams/triangular":"random.streams.triangular","@stdlib/random/streams/uniform":"random.streams.uniform","@stdlib/random/streams/weibull":"random.streams.weibull","@stdlib/random/strided/arcsine":"random.strided.arcsine","@stdlib/random/strided/bernoulli":"random.strided.bernoulli","@stdlib/random/strided/beta":"random.strided.beta","@stdlib/random/strided/betaprime":"random.strided.betaprime","@stdlib/random/strided/chi":"random.strided.chi","@stdlib/random/strided/chisquare":"random.strided.chisquare","@stdlib/random/strided/cosine":"random.strided.cosine","@stdlib/random/strided/discrete-uniform":"random.strided.discreteUniform","@stdlib/random/strided/exponential":"random.strided.exponential","@stdlib/random/strided/gamma":"random.strided.gamma","@stdlib/random/strided/geometric":"random.strided.geometric","@stdlib/random/strided/invgamma":"random.strided.invgamma","@stdlib/random/strided/lognormal":"random.strided.lognormal","@stdlib/random/strided/minstd":"random.strided.minstd","@stdlib/random/strided/minstd-shuffle":"random.strided.minstdShuffle","@stdlib/random/strided/mt19937":"random.strided.mt19937","@stdlib/random/strided/normal":"random.strided.normal","@stdlib/random/strided/poisson":"random.strided.poisson","@stdlib/random/strided/randu":"random.strided.randu","@stdlib/random/strided/rayleigh":"random.strided.rayleigh","@stdlib/random/strided/t":"random.strided.t","@stdlib/random/strided/uniform":"random.strided.uniform","@stdlib/random/strided/weibull":"random.strided.weibull","@stdlib/stats/ranks":"ranks","@stdlib/fs/read-dir":"readDir","@stdlib/fs/read-file":"readFile","@stdlib/fs/read-file-list":"readFileList","@stdlib/fs/read-json":"readJSON","@stdlib/fs/read-wasm":"readWASM","@stdlib/complex/float64/real":"real","@stdlib/array/typed-real":"realarray","@stdlib/array/typed-real-ctors":"realarrayCtors","@stdlib/array/typed-real-dtypes":"realarrayDataTypes","@stdlib/complex/float32/real":"realf","@stdlib/utils/real-max":"realmax","@stdlib/utils/real-min":"realmin","@stdlib/regexp/basename":"reBasename","@stdlib/regexp/basename-posix":"reBasenamePosix","@stdlib/regexp/basename-windows":"reBasenameWindows","@stdlib/regexp/color-hexadecimal":"reColorHexadecimal","@stdlib/regexp/decimal-number":"reDecimalNumber","@stdlib/regexp/dirname":"reDirname","@stdlib/regexp/dirname-posix":"reDirnamePosix","@stdlib/regexp/dirname-windows":"reDirnameWindows","@stdlib/utils/reduce":"reduce","@stdlib/utils/reduce2d":"reduce2d","@stdlib/utils/async/reduce":"reduceAsync","@stdlib/utils/reduce-right":"reduceRight","@stdlib/utils/async/reduce-right":"reduceRightAsync","@stdlib/regexp/duration-string":"reDurationString","@stdlib/regexp/eol":"reEOL","@stdlib/regexp/extended-length-path":"reExtendedLengthPath","@stdlib/regexp/extname":"reExtname","@stdlib/regexp/extname-posix":"reExtnamePosix","@stdlib/regexp/extname-windows":"reExtnameWindows","@stdlib/regexp/filename":"reFilename","@stdlib/regexp/filename-posix":"reFilenamePosix","@stdlib/regexp/filename-windows":"reFilenameWindows","@stdlib/utils/regexp-from-string":"reFromString","@stdlib/regexp/function-name":"reFunctionName","@stdlib/regexp/to-json":"regexp2json","@stdlib/complex/float64/reim":"reim","@stdlib/complex/float32/reim":"reimf","@stdlib/utils/reject-arguments":"rejectArguments","@stdlib/string/remove-first":"removeFirst","@stdlib/string/remove-last":"removeLast","@stdlib/string/remove-punctuation":"removePunctuation","@stdlib/string/remove-utf8-bom":"removeUT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","@stdlib/complex/float32/base/mul":"base.cmulf","@stdlib/complex/float64/base/neg":"base.cneg","@stdlib/complex/float32/base/neg":"base.cnegf","@stdlib/string/base/code-point-at":"base.codePointAt","@stdlib/string/base/constantcase":"base.constantcase","@stdlib/math/base/tools/continued-fraction":"base.continuedFraction","@stdlib/math/base/special/copysign":"base.copysign","@stdlib/math/base/special/copysignf":"base.copysignf","@stdlib/math/base/special/cos":"base.cos","@stdlib/math/base/special/cosd":"base.cosd","@stdlib/math/base/special/cosh":"base.cosh","@stdlib/math/base/special/cosm1":"base.cosm1","@stdlib/math/base/special/cospi":"base.cospi","@stdlib/math/base/special/cot":"base.cot","@stdlib/math/base/special/cotd":"base.cotd","@stdlib/math/base/special/coth":"base.coth","@stdlib/math/base/special/covercos":"base.covercos","@stdlib/math/base/special/coversin":"base.coversin","@stdlib/math/base/special/cphase":"base.cphase","@stdlib/math/base/special/cpolar":"base.cpolar","@stdlib/math/base/special/cround":"base.cround","@stdlib/math/base/special/croundn":"base.croundn","@stdlib/math/base/special/csc":"base.csc","@stdlib/math/base/special/cscd":"base.cscd","@stdlib/math/base/special/csch":"base.csch","@stdlib/math/base/special/csignum":"base.csignum","@stdlib/complex/float64/base/sub":"base.csub","@stdlib/complex/float32/base/sub":"base.csubf","@stdlib/math/base/special/deg2rad":"base.deg2rad","@stdlib/math/base/special/deg2radf":"base.deg2radf","@stdlib/math/base/special/digamma":"base.digamma","@stdlib/math/base/special/dirac-delta":"base.diracDelta","@stdlib/number/float64/base/div":"base.div","@stdlib/number/float32/base/div":"base.divf","@stdlib/string/base/dotcase":"base.dotcase","@stdlib/stats/base/dists/arcsine/ctor":"base.dists.arcsine.Arcsine","@stdlib/stats/base/dists/arcsine/cdf":"base.dists.arcsine.cdf","@stdlib/stats/base/dists/arcsine/entropy":"base.dists.arcsine.entropy","@stdlib/stats/base/dists/arcsine/kurtosis":"base.dists.arcsine.kurtosis","@stdlib/stats/base/dists/arcsine/logcdf":"base.dists.arcsine.logcdf","@stdlib/stats/base/dists/arcsine/logpdf":"base.dists.arcsine.logpdf","@stdlib/stats/base/dists/arcsine/mean":"base.dists.arcsine.mean","@stdlib/stats/base/dists/arcsine/median":"base.dists.arcsine.median","@stdlib/stats/base/dists/arcsine/mode":"base.dists.arcsine.mode","@stdlib/stats/base/dists/arcsine/pdf":"base.dists.arcsine.pdf","@stdlib/stats/base/dists/arcsine/quantile":"base.dists.arcsine.quantile","@stdlib/stats/base/dists/arcsine/skewness":"base.dists.arcsine.skewness","@stdlib/stats/base/dists/arcsine/stdev":"base.dists.arcsine.stdev","@stdlib/stats/base/dists/arcsine/variance":"base.dists.arcsine.variance","@stdlib/stats/base/dists/bernoulli/ctor":"base.dists.bernoulli.Bernoulli","@stdlib/stats/base/dists/bernoulli/cdf":"base.dists.bernoulli.cdf","@stdlib/stats/base/dists/bernoulli/entropy":"base.dists.bernoulli.entropy","@stdlib/stats/base/dists/bernoulli/kurtosis":"base.dists.bernoulli.kurtosis","@stdlib/stats/base/dists/bernoulli/mean":"base.dists.bernoulli.mean","@stdlib/stats/base/dists/bernoulli/median":"base.dists.bernoulli.median","@stdlib/stats/base/dists/bernoulli/mgf":"base.dists.bernoulli.mgf","@stdlib/stats/base/dists/bernoulli/mode":"base.dists.bernoulli.mode","@stdlib/stats/base/dists/bernoulli/pmf":"base.dists.bernoulli.pmf","@stdlib/stats/base/dists/bernoulli/quantile":"base.dists.bernoulli.quantile","@stdlib/stats/base/dists/bernoulli/skewness":"base.dists.bernoulli.skewness","@stdlib/stats/base/dists/bernoulli/stdev":"base.dists.bernoulli.stdev","@stdlib/stats/base/dists/bernoulli/variance":"base.dists.bernoulli.variance","@stdlib/stats/base/dists/beta/ctor":"base.dists.beta.Beta","@stdlib/stats/base/dists/beta/cdf":"base.dists.beta.cdf","@stdlib/stats/base/dists/beta/entropy":"base.dists.beta.entropy","@stdlib/stats/base/dists/beta/kurtosis":"base.dists.beta.kurtosis","@stdlib/stats/base/dists/beta/logcdf":"base.dists.beta.logcdf","@stdlib/stats/base/dists/beta/logpdf":"base.dists.beta.logpdf","@stdlib/stats/base/dists/beta/mean":"base.dists.beta.mean","@stdlib/stats/base/dists/beta/median":"base.dists.beta.median","@stdlib/stats/base/dists/beta/mgf":"base.dists.beta.mgf","@stdlib/stats/base/dists/beta/mode":"base.dists.beta.mode","@stdlib/stats/base/dists/beta/pdf":"base.dists.beta.pdf","@stdlib/stats/base/dists/beta/quantile":"base.dists.beta.quantile","@stdlib/stats/base/dists/beta/skewness":"base.dists.beta.skewness","@stdlib/stats/base/dists/beta/stdev":"base.dists.beta.stdev","@stdlib/stats/base/dists/beta/variance":"base.dists.beta.variance","@stdlib/stats/base/dists/betaprime/ctor":"base.dists.betaprime.BetaPrime","@stdlib/stats/base/dists/betaprime/cdf":"base.dists.betaprime.cdf","@stdlib/stats/base/dists/betaprime/kurtosis":"base.dists.betaprime.kurtosis","@stdlib/stats/base/dists/betaprime/logcdf":"base.dists.betaprime.logcdf","@stdlib/stats/base/dists/betaprime/logpdf":"base.dists.betaprime.logpdf","@stdlib/stats/base/dists/betapr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tf8-bom":"removeUTF8BOM","@stdlib/string/remove-words":"removeWords","@stdlib/fs/rename":"rename","@stdlib/regexp/native-function":"reNativeFunction","@stdlib/utils/reorder-arguments":"reorderArguments","@stdlib/string/repeat":"repeat","@stdlib/string/replace":"replace","@stdlib/string/replace-before":"replaceBefore","@stdlib/regexp/regexp":"reRegExp","@stdlib/utils/escape-regexp-string":"rescape","@stdlib/regexp/semver":"reSemVer","@stdlib/fs/resolve-parent-path":"resolveParentPath","@stdlib/fs/resolve-parent-path-by":"resolveParentPathBy","@stdlib/regexp/unc-path":"reUncPath","@stdlib/regexp/utf16-surrogate-pair":"reUtf16SurrogatePair","@stdlib/regexp/utf16-unpaired-surrogate":"reUtf16UnpairedSurrogate","@stdlib/utils/reverse-arguments":"reverseArguments","@stdlib/string/reverse":"reverseString","@stdlib/random/base/reviver":"reviveBasePRNG","@stdlib/buffer/reviver":"reviveBuffer","@stdlib/complex/reviver":"reviveComplex","@stdlib/complex/float32/reviver":"reviveComplex64","@stdlib/complex/float64/reviver":"reviveComplex128","@stdlib/error/reviver":"reviveError","@stdlib/regexp/reviver":"reviveRegExp","@stdlib/array/reviver":"reviveTypedArray","@stdlib/regexp/whitespace":"reWhitespace","@stdlib/string/right-pad":"rpad","@stdlib/string/right-trim":"rtrim","@stdlib/string/right-trim-n":"rtrimN","@stdlib/utils/safe-int-max":"safeintmax","@stdlib/utils/safe-int-min":"safeintmin","@stdlib/random/sample":"sample","@stdlib/datasets/savoy-stopwords-fin":"SAVOY_STOPWORDS_FIN","@stdlib/datasets/savoy-stopwords-fr":"SAVOY_STOPWORDS_FR","@stdlib/datasets/savoy-stopwords-ger":"SAVOY_STOPWORDS_GER","@stdlib/datasets/savoy-stopwords-it":"SAVOY_STOPWORDS_IT","@stdlib/datasets/savoy-stopwords-por":"SAVOY_STOPWORDS_POR","@stdlib/datasets/savoy-stopwords-sp":"SAVOY_STOPWORDS_SP","@stdlib/datasets/savoy-stopwords-swe":"SAVOY_STOPWORDS_SWE","@stdlib/array/from-scalar":"scalar2array","@stdlib/ndarray/from-scalar":"scalar2ndarray","@stdlib/blas/sdot":"sdot","@stdlib/constants/time/seconds-in-day":"SECONDS_IN_DAY","@stdlib/constants/time/seconds-in-hour":"SECONDS_IN_HOUR","@stdlib/constants/time/seconds-in-minute":"SECONDS_IN_MINUTE","@stdlib/constants/time/seconds-in-week":"SECONDS_IN_WEEK","@stdlib/time/seconds-in-month":"secondsInMonth","@stdlib/time/seconds-in-year":"secondsInYear","@stdlib/nlp/sentencize":"sentencize","@stdlib/slice/seq2slice":"seq2slice","@stdlib/utils/define-configurable-read-only-property":"setConfigurableReadOnly","@stdlib/utils/define-configurable-read-only-accessor":"setConfigurableReadOnlyAccessor","@stdlib/utils/define-configurable-read-write-accessor":"setConfigurableReadWriteAccessor","@stdlib/utils/define-configurable-write-only-accessor":"setConfigurableWriteOnlyAccessor","@stdlib/utils/define-memoized-configurable-read-only-property":"setMemoizedConfigurableReadOnly","@stdlib/utils/define-memoized-read-only-property":"setMemoizedReadOnly","@stdlib/utils/define-nonenumerable-property":"setNonEnumerableProperty","@stdlib/utils/define-nonenumerable-read-only-property":"setNonEnumerableReadOnly","@stdlib/utils/define-nonenumerable-read-only-accessor":"setNonEnumerableReadOnlyAccessor","@stdlib/utils/define-nonenumerable-read-write-accessor":"setNonEnumerableReadWriteAccessor","@stdlib/utils/define-nonenumerable-write-only-accessor":"setNonEnumerableWriteOnlyAccessor","@stdlib/utils/define-read-only-property":"setReadOnly","@stdlib/utils/define-read-only-accessor":"setReadOnlyAccessor","@stdlib/utils/define-read-write-accessor":"setReadWriteAccessor","@stdlib/utils/define-write-only-accessor":"setWriteOnlyAccessor","@stdlib/array/shared-buffer":"SharedArrayBuffer","@stdlib/utils/shift":"shift","@stdlib/random/shuffle":"shuffle","@stdlib/utils/size-of":"sizeOf","@stdlib/slice/ctor":"Slice","@stdlib/string/snakecase":"snakecase","@stdlib/utils/some":"some","@stdlib/utils/some-by":"someBy","@stdlib/utils/async/some-by":"someByAsync","@stdlib/utils/some-by-right":"someByRight","@stdlib/utils/async/some-by-right":"someByRightAsync","@stdlib/object/some-in-by":"someInBy","@stdlib/utils/some-own-by":"someOwnBy","@stdlib/datasets/sotu":"SOTU","@stdlib/datasets/spache-revised":"SPACHE_REVISED","@stdlib/datasets/spam-assassin":"SPAM_ASSASSIN","@stdlib/plot/sparklines/base/ctor":"SparklineBase","@stdlib/array/to-sparse-iterator":"sparsearray2iterator","@stdlib/array/to-sparse-iterator-right":"sparsearray2iteratorRight","@stdlib/streams/node/split":"splitStream","@stdlib/constants/float64/sqrt-eps":"SQRT_EPS","@stdlib/constants/float64/sqrt-half":"SQRT_HALF","@stdlib/constants/float64/sqrt-half-pi":"SQRT_HALF_PI","@stdlib/constants/float64/sqrt-phi":"SQRT_PHI","@stdlib/constants/float64/sqrt-pi":"SQRT_PI","@stdlib/constants/float64/sqrt-three":"SQRT_THREE","@stdlib/constants/float64/sqrt-two":"SQRT_TWO","@stdlib/constants/float64/sqrt-two-pi":"SQRT_TWO_PI","@stdlib/datasets/ssa-us-births-2000-2014":"SSA_US_BIRTHS_2000_2014","@stdlib/blas/sswap":"sswap","@stdlib/dstructs/stack":"Stack","@stdlib/namespace/standalone2pkg":"standalone2pkg","@stdlib/datasets/standard-card-deck":"STANDARD_CARD_DECK","@stdlib/string/startcase":"startcase","@stdlib/string/starts-with":"startsWith","@stdlib/datasets/stopwords-en":"STOPWORDS_EN","@stdlib/math/strided/special/abs":"strided.abs","@stdlib/math/strided/special/abs2":"strided.abs2","@stdlib/math/strided/special/abs2-by":"strided.abs2By","@stdlib/math/strided/special/abs-by":"strided.absBy","@stdlib/math/strided/special/acos-by":"strided.acosBy","@stdlib/math/strided/special/acosh-by":"strided.acoshBy","@stdlib/math/strided/special/acot-by":"strided.acotBy","@stdlib/math/strided/special/acoth-by":"strided.acothBy","@stdlib/math/strided/special/acovercos-by":"strided.acovercosBy","@stdlib/math/strided/special/acoversin-by":"strided.acoversinBy","@stdlib/math/strided/ops/add":"strided.add","@stdlib/math/strided/ops/add-by":"strided.addBy","@stdlib/math/strided/special/ahavercos-by":"strided.ahavercosBy","@stdlib/math/strided/special/ahaversin-by":"strided.ahaversinBy","@stdlib/math/strided/special/asin-by":"strided.asinBy","@stdlib/math/strided/special/asinh-by":"strided.asinhBy","@stdlib/math/strided/special/atan-by":"strided.atanBy","@stdlib/math/strided/special/atanh-by":"strided.atanhBy","@stdlib/math/strided/special/avercos-by":"strided.avercosBy","@stdlib/math/strided/special/aversin-by":"strided.aversinBy","@stdlib/math/strided/special/besselj0-by":"strided.besselj0By","@stdlib/math/strided/special/besselj1-by":"strided.besselj1By","@stdlib/math/strided/special/bessely0-by":"strided.bessely0By","@stdlib/math/strided/special/bessely1-by":"strided.bessely1By","@stdlib/math/strided/special/binet-by":"strided.binetBy","@stdlib/math/strided/special/cbrt":"strided.cbrt","@stdlib/math/strided/special/cbrt-by":"strided.cbrtBy","@stdlib/math/strided/special/ceil":"strided.ceil","@stdlib/math/strided/special/cos-by":"strided.cosBy","@stdlib/math/strided/special/deg2rad":"strided.deg2rad","@stdlib/strided/dtypes":"strided.dataTypes","@stdlib/math/strided/special/dcbrt-by":"strided.dcbrtBy","@stdlib/strided/dispatch":"strided.dispatch","@stdlib/strided/dispatch-by":"strided.dispatchBy","@stdlib/math/strided/special/floor":"strided.floor","@stdlib/math/strided/special/inv":"strided.inv","@stdlib/math/strided/ops/mul":"strided.mul","@stdlib/math/strided/ops/mul-by":"strided.mulBy","@stdlib/math/strided/special/ramp":"strided.ramp","@stdlib/math/strided/special/rsqrt":"strided.rsqrt","@stdlib/math/strided/special/sin-by":"strided.sinBy","@stdlib/math/strided/special/sqrt":"strided.sqrt","@stdlib/math/strided/special/sqrt-by":"strided.sqrtBy","@stdlib/math/strided/ops/sub":"strided.sub","@stdlib/math/strided/ops/sub-by":"strided.subBy","@stdlib/math/strided/special/trunc":"strided.trunc","@stdlib/array/to-strided-iterator":"stridedarray2iterator","@stdlib/streams/node/from-strided-array":"stridedArrayStream","@stdlib/buffer/from-string":"string2buffer","@stdlib/dstructs/struct":"structFactory","@stdlib/ndarray/sub2ind":"sub2ind","@stdlib/string/substring-after":"substringAfter","@stdlib/string/substring-after-last":"substringAfterLast","@stdlib/string/substring-before":"substringBefore","@stdlib/string/substring-before-last":"substringBeforeLast","@stdlib/datasets/suthaharan-multi-hop-sensor-network":"SUTHAHARAN_MULTI_HOP_SENSOR_NETWORK","@stdlib/datasets/suthaharan-single-hop-sensor-network":"SUTHAHARAN_SINGLE_HOP_SENSOR_NETWORK","@stdlib/symbol/ctor":"Symbol","@stdlib/utils/tabulate":"tabulate","@stdlib/utils/tabulate-by":"tabulateBy","@stdlib/utils/async/tabulate-by":"tabulateByAsync","@stdlib/function/thunk":"thunk","@stdlib/time/tic":"tic","@stdlib/utils/timeit":"timeit","@stdlib/os/tmpdir":"tmpdir","@stdlib/time/toc":"toc","@stdlib/nlp/tokenize":"tokenize","@stdlib/streams/node/transform":"transformStream","@stdlib/string/trim":"trim","@stdlib/string/truncate":"truncate","@stdlib/string/truncate-middle":"truncateMiddle","@stdlib/utils/try-catch":"trycatch","@stdlib/utils/async/try-catch":"trycatchAsync","@stdlib/utils/try-function":"tryFunction","@stdlib/utils/try-require":"tryRequire","@stdlib/utils/try-then":"trythen","@stdlib/utils/async/try-then":"trythenAsync","@stdlib/stats/ttest":"ttest","@stdlib/stats/ttest2":"ttest2","@stdlib/constants/float64/two-pi":"TWO_PI","@stdlib/array/typed":"typedarray","@stdlib/array/to-json":"typedarray2json","@stdlib/array/typed-ctors":"typedarrayCtors","@stdlib/array/typed-dtypes":"typedarrayDataTypes","@stdlib/array/pool":"typedarraypool","@stdlib/utils/type-max":"typemax","@stdlib/utils/type-min":"typemin","@stdlib/utils/type-of":"typeOf","@stdlib/constants/uint8/max":"UINT8_MAX","@stdlib/constants/uint8/num-bytes":"UINT8_NUM_BYTES","@stdlib/array/uint8":"Uint8Array","@stdlib/array/uint8c":"Uint8ClampedArray","@stdlib/constants/uint16/max":"UINT16_MAX","@stdlib/constants/uint16/num-bytes":"UINT16_NUM_BYTES","@stdlib/array/uint16":"Uint16Array","@stdlib/constants/uint32/max":"UINT32_MAX","@stdlib/constants/uint32/num-bytes":"UINT32_NUM_BYTES","@stdlib/array/uint32":"Uint32Array","@stdlib/process/umask":"umask","@stdlib/string/uncapitalize":"uncapitalize","@stdlib/utils/uncapitalize-keys":"uncapitalizeKeys","@stdlib/utils/uncurry":"uncurry","@stdlib/utils/uncurry-right":"uncurryRight","@stdlib/constants/unicode/max":"UNICODE_MAX","@stdlib/constants/unicode/max-bmp":"UNICODE_MAX_BMP","@stdlib/plot/sparklines/unicode/column":"UnicodeColumnChartSparkline","@stdlib/plot/sparklines/unicode/line":"UnicodeLineChartSparkline","@stdlib/plot/sparklines/unicode":"UnicodeSparkline","@stdlib/plot/sparklines/unicode/tristate":"UnicodeTristateChartSparkline","@stdlib/plot/sparklines/unicode/up-down":"UnicodeUpDownChartSparkline","@stdlib/plot/sparklines/unicode/win-loss":"UnicodeWinLossChartSparkline","@stdlib/fs/unlink":"unlink","@stdlib/utils/unshift":"unshift","@stdlib/utils/until":"until","@stdlib/utils/async/until":"untilAsync","@stdlib/utils/until-each":"untilEach","@stdlib/utils/until-each-right":"untilEachRight","@stdlib/utils/unzip":"unzip","@stdlib/string/uppercase":"uppercase","@stdlib/utils/uppercase-keys":"uppercaseKeys","@stdlib/datasets/us-states-abbr":"US_STATES_ABBR","@stdlib/datasets/us-states-capitals":"US_STATES_CAPITALS","@stdlib/datasets/us-states-capitals-names":"US_STATES_CAPITALS_NAMES","@stdlib/datasets/us-states-names":"US_STATES_NAMES","@stdlib/datasets/us-states-names-capitals":"US_STATES_NAMES_CAPITALS","@stdlib/string/utf16-to-utf8-array":"utf16ToUTF8Array","@stdlib/stats/vartest":"vartest","@stdlib/utils/async/series-waterfall":"waterfall","@stdlib/wasm/memory":"WebAssemblyMemory","@stdlib/utils/async/while":"whileAsync","@stdlib/utils/while-each":"whileEach","@stdlib/utils/while-each-right":"whileEachRight","@stdlib/utils/while":"whilst","@stdlib/stats/wilcoxon":"wilcoxon","@stdlib/utils/writable-properties":"writableProperties","@stdlib/utils/writable-properties-in":"writablePropertiesIn","@stdlib/utils/writable-property-names":"writablePropertyNames","@stdlib/utils/writable-property-names-in":"writablePropertyNamesIn","@stdlib/utils/writable-property-symbols":"writablePropertySymbols","@stdlib/utils/writable-property-symbols-in":"writablePropertySymbolsIn","@stdlib/fs/write-file":"writeFile","@stdlib/utils/zip":"zip","@stdlib/stats/ztest":"ztest","@stdlib/stats/ztest2":"ztest2"}
diff --git a/lib/node_modules/@stdlib/namespace/pkg2related/data/data.csv b/lib/node_modules/@stdlib/namespace/pkg2related/data/data.csv
index a0d821162415..d3070c71194b 100644
--- a/lib/node_modules/@stdlib/namespace/pkg2related/data/data.csv
+++ b/lib/node_modules/@stdlib/namespace/pkg2related/data/data.csv
@@ -1170,12 +1170,12 @@
"@stdlib/blas/ext/base/dssum","@stdlib/blas/ext/base/dsnansum,@stdlib/blas/ext/base/sdssum,@stdlib/blas/ext/base/ssum,@stdlib/stats/strided/dsmean"
"@stdlib/blas/ext/base/dssumors","@stdlib/blas/ext/base/dsnansumors,@stdlib/blas/ext/base/dssum,@stdlib/blas/ext/base/dsumors,@stdlib/blas/ext/base/ssumors"
"@stdlib/blas/ext/base/dssumpw","@stdlib/blas/ext/base/dsnansumpw,@stdlib/blas/ext/base/dssum,@stdlib/blas/ext/base/dsumpw,@stdlib/blas/ext/base/ssumpw"
-"@stdlib/stats/strided/dstdev","@stdlib/stats/strided/dnanstdev,@stdlib/stats/strided/dvariance,@stdlib/stats/strided/sstdev,@stdlib/stats/base/stdev"
-"@stdlib/stats/strided/dstdevch","@stdlib/stats/strided/dnanstdevch,@stdlib/stats/strided/dstdev,@stdlib/stats/strided/dvariancech,@stdlib/stats/strided/sstdevch,@stdlib/stats/base/stdevch"
-"@stdlib/stats/strided/dstdevpn","@stdlib/stats/strided/dnanstdevpn,@stdlib/stats/strided/dstdev,@stdlib/stats/strided/dvariancepn,@stdlib/stats/strided/sstdevpn,@stdlib/stats/base/stdevpn"
-"@stdlib/stats/strided/dstdevtk","@stdlib/stats/strided/dnanstdevtk,@stdlib/stats/strided/dstdev,@stdlib/stats/strided/dvariancetk,@stdlib/stats/strided/sstdevtk,@stdlib/stats/base/stdevtk"
-"@stdlib/stats/strided/dstdevwd","@stdlib/stats/strided/dnanstdevwd,@stdlib/stats/strided/dstdev,@stdlib/stats/strided/dvariancewd,@stdlib/stats/base/sstdevwd,@stdlib/stats/base/stdevwd"
-"@stdlib/stats/strided/dstdevyc","@stdlib/stats/strided/dnanstdevyc,@stdlib/stats/strided/dstdev,@stdlib/stats/strided/dvarianceyc,@stdlib/stats/strided/sstdevyc,@stdlib/stats/base/stdevyc"
+"@stdlib/stats/strided/dstdev","@stdlib/stats/strided/dnanstdev,@stdlib/stats/strided/dvariance,@stdlib/stats/strided/sstdev,@stdlib/stats/strided/stdev"
+"@stdlib/stats/strided/dstdevch","@stdlib/stats/strided/dnanstdevch,@stdlib/stats/strided/dstdev,@stdlib/stats/strided/dvariancech,@stdlib/stats/strided/sstdevch,@stdlib/stats/strided/stdevch"
+"@stdlib/stats/strided/dstdevpn","@stdlib/stats/strided/dnanstdevpn,@stdlib/stats/strided/dstdev,@stdlib/stats/strided/dvariancepn,@stdlib/stats/strided/sstdevpn,@stdlib/stats/strided/stdevpn"
+"@stdlib/stats/strided/dstdevtk","@stdlib/stats/strided/dnanstdevtk,@stdlib/stats/strided/dstdev,@stdlib/stats/strided/dvariancetk,@stdlib/stats/strided/sstdevtk,@stdlib/stats/strided/stdevtk"
+"@stdlib/stats/strided/dstdevwd","@stdlib/stats/strided/dnanstdevwd,@stdlib/stats/strided/dstdev,@stdlib/stats/strided/dvariancewd,@stdlib/stats/base/sstdevwd,@stdlib/stats/strided/stdevwd"
+"@stdlib/stats/strided/dstdevyc","@stdlib/stats/strided/dnanstdevyc,@stdlib/stats/strided/dstdev,@stdlib/stats/strided/dvarianceyc,@stdlib/stats/strided/sstdevyc,@stdlib/stats/strided/stdevyc"
"@stdlib/blas/ext/base/dsum","@stdlib/blas/base/dasum,@stdlib/stats/strided/dmean,@stdlib/blas/ext/base/dnansum,@stdlib/blas/ext/base/ssum,@stdlib/blas/ext/base/gsum"
"@stdlib/blas/ext/base/dsumkbn","@stdlib/blas/ext/base/dnansumkbn,@stdlib/blas/ext/base/dsum,@stdlib/blas/ext/base/dsumkbn2,@stdlib/blas/ext/base/dsumors,@stdlib/blas/ext/base/dsumpw,@stdlib/blas/ext/base/gsumkbn,@stdlib/blas/ext/base/ssumkbn"
"@stdlib/blas/ext/base/dsumkbn2","@stdlib/blas/ext/base/dnansumkbn2,@stdlib/blas/ext/base/dsum,@stdlib/blas/ext/base/dsumkbn,@stdlib/blas/ext/base/dsumors,@stdlib/blas/ext/base/dsumpw,@stdlib/blas/ext/base/gsumkbn2,@stdlib/blas/ext/base/ssumkbn2"
@@ -1280,12 +1280,12 @@
"@stdlib/stats/strided/nanmskrange","@stdlib/stats/strided/dnanmskrange,@stdlib/stats/strided/mskrange,@stdlib/stats/strided/nanrange,@stdlib/stats/strided/nanmskmax,@stdlib/stats/strided/nanmskmin,@stdlib/stats/strided/snanmskrange"
"@stdlib/stats/strided/nanrange","@stdlib/stats/strided/dnanrange,@stdlib/stats/strided/nanmax,@stdlib/stats/strided/nanmin,@stdlib/stats/strided/range,@stdlib/stats/strided/snanrange"
"@stdlib/stats/strided/nanrange-by","@stdlib/stats/strided/dnanrange,@stdlib/stats/strided/nanmax-by,@stdlib/stats/strided/nanmin-by,@stdlib/stats/strided/nanrange,@stdlib/stats/strided/range-by,@stdlib/stats/strided/snanrange"
-"@stdlib/stats/base/nanstdev","@stdlib/stats/strided/dnanstdev,@stdlib/stats/base/nanvariance,@stdlib/stats/base/snanstdev,@stdlib/stats/base/stdev"
-"@stdlib/stats/base/nanstdevch","@stdlib/stats/strided/dnanstdevch,@stdlib/stats/base/nanvariancech,@stdlib/stats/base/nanstdev,@stdlib/stats/base/snanstdevch,@stdlib/stats/base/stdevch"
-"@stdlib/stats/base/nanstdevpn","@stdlib/stats/strided/dnanstdevpn,@stdlib/stats/base/nanvariancepn,@stdlib/stats/base/nanstdev,@stdlib/stats/base/snanstdevpn,@stdlib/stats/base/stdevpn"
-"@stdlib/stats/base/nanstdevtk","@stdlib/stats/strided/dnanstdevtk,@stdlib/stats/base/nanvariancetk,@stdlib/stats/base/nanstdev,@stdlib/stats/base/snanstdevtk,@stdlib/stats/base/stdevtk"
-"@stdlib/stats/base/nanstdevwd","@stdlib/stats/strided/dnanstdevwd,@stdlib/stats/base/nanvariancewd,@stdlib/stats/base/nanstdev,@stdlib/stats/base/snanstdevwd,@stdlib/stats/base/stdevwd"
-"@stdlib/stats/base/nanstdevyc","@stdlib/stats/strided/dnanstdevyc,@stdlib/stats/base/nanvarianceyc,@stdlib/stats/base/nanstdev,@stdlib/stats/base/snanstdevyc,@stdlib/stats/base/stdevyc"
+"@stdlib/stats/base/nanstdev","@stdlib/stats/strided/dnanstdev,@stdlib/stats/base/nanvariance,@stdlib/stats/base/snanstdev,@stdlib/stats/strided/stdev"
+"@stdlib/stats/base/nanstdevch","@stdlib/stats/strided/dnanstdevch,@stdlib/stats/base/nanvariancech,@stdlib/stats/base/nanstdev,@stdlib/stats/base/snanstdevch,@stdlib/stats/strided/stdevch"
+"@stdlib/stats/base/nanstdevpn","@stdlib/stats/strided/dnanstdevpn,@stdlib/stats/base/nanvariancepn,@stdlib/stats/base/nanstdev,@stdlib/stats/base/snanstdevpn,@stdlib/stats/strided/stdevpn"
+"@stdlib/stats/base/nanstdevtk","@stdlib/stats/strided/dnanstdevtk,@stdlib/stats/base/nanvariancetk,@stdlib/stats/base/nanstdev,@stdlib/stats/base/snanstdevtk,@stdlib/stats/strided/stdevtk"
+"@stdlib/stats/base/nanstdevwd","@stdlib/stats/strided/dnanstdevwd,@stdlib/stats/base/nanvariancewd,@stdlib/stats/base/nanstdev,@stdlib/stats/base/snanstdevwd,@stdlib/stats/strided/stdevwd"
+"@stdlib/stats/base/nanstdevyc","@stdlib/stats/strided/dnanstdevyc,@stdlib/stats/base/nanvarianceyc,@stdlib/stats/base/nanstdev,@stdlib/stats/base/snanstdevyc,@stdlib/stats/strided/stdevyc"
"@stdlib/stats/base/nanvariance","@stdlib/stats/strided/dnanvariance,@stdlib/stats/base/nanstdev,@stdlib/stats/base/snanvariance,@stdlib/stats/strided/variance"
"@stdlib/stats/base/nanvariancech","@stdlib/stats/strided/dnanvariancech,@stdlib/stats/base/nanstdevch,@stdlib/stats/base/nanvariance,@stdlib/stats/base/snanvariancech,@stdlib/stats/strided/variancech"
"@stdlib/stats/base/nanvariancepn","@stdlib/stats/strided/dnanvariancepn,@stdlib/stats/base/nanstdevpn,@stdlib/stats/base/nanvariance,@stdlib/stats/base/snanvariancepn,@stdlib/stats/strided/variancepn"
@@ -1418,24 +1418,24 @@
"@stdlib/blas/ext/base/ssortins","@stdlib/blas/ext/base/dsortins,@stdlib/blas/ext/base/gsortins,@stdlib/blas/ext/base/ssort2ins"
"@stdlib/blas/ext/base/ssortsh","@stdlib/blas/ext/base/dsortsh,@stdlib/blas/ext/base/gsortsh,@stdlib/blas/ext/base/ssort2sh"
"@stdlib/math/strided/special/ssqrt","@stdlib/math/strided/special/dsqrt,@stdlib/math/strided/special/scbrt,@stdlib/math/strided/special/sqrt,@stdlib/math/strided/special/srsqrt"
-"@stdlib/stats/strided/sstdev","@stdlib/stats/strided/dstdev,@stdlib/stats/base/snanstdev,@stdlib/stats/base/stdev,@stdlib/stats/strided/svariance"
-"@stdlib/stats/strided/sstdevch","@stdlib/stats/strided/dstdevch,@stdlib/stats/base/snanstdevch,@stdlib/stats/strided/sstdev,@stdlib/stats/base/stdevch,@stdlib/stats/strided/svariancech"
-"@stdlib/stats/strided/sstdevpn","@stdlib/stats/strided/dstdevpn,@stdlib/stats/base/snanstdevpn,@stdlib/stats/strided/sstdev,@stdlib/stats/base/stdevpn,@stdlib/stats/strided/svariancepn"
-"@stdlib/stats/strided/sstdevtk","@stdlib/stats/strided/dstdevtk,@stdlib/stats/base/snanstdevtk,@stdlib/stats/strided/sstdev,@stdlib/stats/base/stdevtk,@stdlib/stats/strided/svariancetk"
-"@stdlib/stats/base/sstdevwd","@stdlib/stats/strided/dstdevwd,@stdlib/stats/base/snanstdevwd,@stdlib/stats/strided/sstdev,@stdlib/stats/base/stdevwd,@stdlib/stats/strided/svariancewd"
-"@stdlib/stats/strided/sstdevyc","@stdlib/stats/strided/dstdevyc,@stdlib/stats/base/snanstdevyc,@stdlib/stats/strided/sstdev,@stdlib/stats/base/stdevyc,@stdlib/stats/strided/svarianceyc"
+"@stdlib/stats/strided/sstdev","@stdlib/stats/strided/dstdev,@stdlib/stats/base/snanstdev,@stdlib/stats/strided/stdev,@stdlib/stats/strided/svariance"
+"@stdlib/stats/strided/sstdevch","@stdlib/stats/strided/dstdevch,@stdlib/stats/base/snanstdevch,@stdlib/stats/strided/sstdev,@stdlib/stats/strided/stdevch,@stdlib/stats/strided/svariancech"
+"@stdlib/stats/strided/sstdevpn","@stdlib/stats/strided/dstdevpn,@stdlib/stats/base/snanstdevpn,@stdlib/stats/strided/sstdev,@stdlib/stats/strided/stdevpn,@stdlib/stats/strided/svariancepn"
+"@stdlib/stats/strided/sstdevtk","@stdlib/stats/strided/dstdevtk,@stdlib/stats/base/snanstdevtk,@stdlib/stats/strided/sstdev,@stdlib/stats/strided/stdevtk,@stdlib/stats/strided/svariancetk"
+"@stdlib/stats/base/sstdevwd","@stdlib/stats/strided/dstdevwd,@stdlib/stats/base/snanstdevwd,@stdlib/stats/strided/sstdev,@stdlib/stats/strided/stdevwd,@stdlib/stats/strided/svariancewd"
+"@stdlib/stats/strided/sstdevyc","@stdlib/stats/strided/dstdevyc,@stdlib/stats/base/snanstdevyc,@stdlib/stats/strided/sstdev,@stdlib/stats/strided/stdevyc,@stdlib/stats/strided/svarianceyc"
"@stdlib/blas/ext/base/ssum","@stdlib/blas/ext/base/dsum,@stdlib/stats/strided/smean,@stdlib/blas/ext/base/snansum,@stdlib/blas/ext/base/gsum"
"@stdlib/blas/ext/base/ssumkbn","@stdlib/blas/ext/base/dsumkbn,@stdlib/blas/ext/base/gsumkbn,@stdlib/blas/ext/base/snansumkbn,@stdlib/blas/ext/base/ssum,@stdlib/blas/ext/base/ssumkbn2,@stdlib/blas/ext/base/ssumors,@stdlib/blas/ext/base/ssumpw"
"@stdlib/blas/ext/base/ssumkbn2","@stdlib/blas/ext/base/dsumkbn2,@stdlib/blas/ext/base/gsumkbn2,@stdlib/blas/ext/base/snansumkbn2,@stdlib/blas/ext/base/ssum,@stdlib/blas/ext/base/ssumkbn,@stdlib/blas/ext/base/ssumors,@stdlib/blas/ext/base/ssumpw"
"@stdlib/blas/ext/base/ssumors","@stdlib/blas/ext/base/dsumors,@stdlib/blas/ext/base/gsumors,@stdlib/blas/ext/base/snansumors,@stdlib/blas/ext/base/ssum,@stdlib/blas/ext/base/ssumkbn2,@stdlib/blas/ext/base/ssumpw"
"@stdlib/blas/ext/base/ssumpw","@stdlib/blas/ext/base/dsumpw,@stdlib/blas/ext/base/gsumpw,@stdlib/blas/ext/base/snansumpw,@stdlib/blas/ext/base/ssum,@stdlib/blas/ext/base/ssumkbn2,@stdlib/blas/ext/base/ssumors"
"@stdlib/blas/base/sswap","@stdlib/blas/base/dswap,@stdlib/blas/base/gswap,@stdlib/blas/base/scopy,@stdlib/blas/sswap"
-"@stdlib/stats/base/stdev","@stdlib/stats/strided/dstdev,@stdlib/stats/base/nanstdev,@stdlib/stats/strided/sstdev,@stdlib/stats/strided/variance"
-"@stdlib/stats/base/stdevch","@stdlib/stats/strided/dstdevch,@stdlib/stats/base/nanstdevch,@stdlib/stats/strided/sstdevch,@stdlib/stats/base/stdev,@stdlib/stats/strided/variancech"
-"@stdlib/stats/base/stdevpn","@stdlib/stats/strided/dstdevpn,@stdlib/stats/base/nanstdevpn,@stdlib/stats/strided/sstdevpn,@stdlib/stats/base/stdev,@stdlib/stats/strided/variancepn"
-"@stdlib/stats/base/stdevtk","@stdlib/stats/strided/dstdevtk,@stdlib/stats/base/nanstdevtk,@stdlib/stats/strided/sstdevtk,@stdlib/stats/base/stdev,@stdlib/stats/strided/variancetk"
-"@stdlib/stats/base/stdevwd","@stdlib/stats/strided/dstdevwd,@stdlib/stats/base/nanstdevwd,@stdlib/stats/base/sstdevwd,@stdlib/stats/base/stdev,@stdlib/stats/strided/variancewd"
-"@stdlib/stats/base/stdevyc","@stdlib/stats/strided/dstdevyc,@stdlib/stats/base/nanstdevyc,@stdlib/stats/strided/sstdevyc,@stdlib/stats/base/stdev,@stdlib/stats/strided/varianceyc"
+"@stdlib/stats/strided/stdev","@stdlib/stats/strided/dstdev,@stdlib/stats/base/nanstdev,@stdlib/stats/strided/sstdev,@stdlib/stats/strided/variance"
+"@stdlib/stats/strided/stdevch","@stdlib/stats/strided/dstdevch,@stdlib/stats/base/nanstdevch,@stdlib/stats/strided/sstdevch,@stdlib/stats/strided/stdev,@stdlib/stats/strided/variancech"
+"@stdlib/stats/strided/stdevpn","@stdlib/stats/strided/dstdevpn,@stdlib/stats/base/nanstdevpn,@stdlib/stats/strided/sstdevpn,@stdlib/stats/strided/stdev,@stdlib/stats/strided/variancepn"
+"@stdlib/stats/strided/stdevtk","@stdlib/stats/strided/dstdevtk,@stdlib/stats/base/nanstdevtk,@stdlib/stats/strided/sstdevtk,@stdlib/stats/strided/stdev,@stdlib/stats/strided/variancetk"
+"@stdlib/stats/strided/stdevwd","@stdlib/stats/strided/dstdevwd,@stdlib/stats/base/nanstdevwd,@stdlib/stats/base/sstdevwd,@stdlib/stats/strided/stdev,@stdlib/stats/strided/variancewd"
+"@stdlib/stats/strided/stdevyc","@stdlib/stats/strided/dstdevyc,@stdlib/stats/base/nanstdevyc,@stdlib/stats/strided/sstdevyc,@stdlib/stats/strided/stdev,@stdlib/stats/strided/varianceyc"
"@stdlib/math/strided/special/strunc","@stdlib/math/strided/special/dtrunc,@stdlib/math/strided/special/sceil,@stdlib/math/strided/special/sfloor,@stdlib/math/strided/special/trunc"
"@stdlib/stats/strided/svariance","@stdlib/stats/strided/dvariance,@stdlib/stats/base/snanvariance,@stdlib/stats/strided/sstdev,@stdlib/stats/strided/variance"
"@stdlib/stats/strided/svariancech","@stdlib/stats/strided/dvariancech,@stdlib/stats/base/snanvariancech,@stdlib/stats/strided/sstdevch,@stdlib/stats/strided/svariance,@stdlib/stats/strided/variancech"
@@ -1448,12 +1448,12 @@
"@stdlib/strided/base/unary-by","@stdlib/strided/base/map-by,@stdlib/strided/base/unary"
"@stdlib/strided/base/unary-dtype-signatures","@stdlib/strided/base/unary-signature-callbacks"
"@stdlib/strided/base/unary-signature-callbacks","@stdlib/strided/base/unary-dtype-signatures"
-"@stdlib/stats/strided/variance","@stdlib/stats/strided/dvariance,@stdlib/stats/base/nanvariance,@stdlib/stats/base/stdev,@stdlib/stats/strided/svariance"
-"@stdlib/stats/strided/variancech","@stdlib/stats/strided/dvariancech,@stdlib/stats/base/nanvariancech,@stdlib/stats/base/stdevch,@stdlib/stats/strided/variance"
-"@stdlib/stats/strided/variancepn","@stdlib/stats/strided/dvariancepn,@stdlib/stats/base/nanvariancepn,@stdlib/stats/base/stdevpn,@stdlib/stats/strided/variance"
-"@stdlib/stats/strided/variancetk","@stdlib/stats/strided/dvariancetk,@stdlib/stats/base/nanvariancetk,@stdlib/stats/base/stdevtk,@stdlib/stats/strided/variance"
-"@stdlib/stats/strided/variancewd","@stdlib/stats/strided/dvariancewd,@stdlib/stats/base/nanvariancewd,@stdlib/stats/base/stdevwd,@stdlib/stats/strided/variance"
-"@stdlib/stats/strided/varianceyc","@stdlib/stats/strided/dvarianceyc,@stdlib/stats/base/nanvarianceyc,@stdlib/stats/base/stdevyc,@stdlib/stats/strided/variance"
+"@stdlib/stats/strided/variance","@stdlib/stats/strided/dvariance,@stdlib/stats/base/nanvariance,@stdlib/stats/strided/stdev,@stdlib/stats/strided/svariance"
+"@stdlib/stats/strided/variancech","@stdlib/stats/strided/dvariancech,@stdlib/stats/base/nanvariancech,@stdlib/stats/strided/stdevch,@stdlib/stats/strided/variance"
+"@stdlib/stats/strided/variancepn","@stdlib/stats/strided/dvariancepn,@stdlib/stats/base/nanvariancepn,@stdlib/stats/strided/stdevpn,@stdlib/stats/strided/variance"
+"@stdlib/stats/strided/variancetk","@stdlib/stats/strided/dvariancetk,@stdlib/stats/base/nanvariancetk,@stdlib/stats/strided/stdevtk,@stdlib/stats/strided/variance"
+"@stdlib/stats/strided/variancewd","@stdlib/stats/strided/dvariancewd,@stdlib/stats/base/nanvariancewd,@stdlib/stats/strided/stdevwd,@stdlib/stats/strided/variance"
+"@stdlib/stats/strided/varianceyc","@stdlib/stats/strided/dvarianceyc,@stdlib/stats/base/nanvarianceyc,@stdlib/stats/strided/stdevyc,@stdlib/stats/strided/variance"
"@stdlib/strided/base/zmap","@stdlib/strided/base/cmap,@stdlib/strided/base/unary"
"@stdlib/slice/base/str2multislice","@stdlib/slice/base/str2slice"
"@stdlib/slice/base/str2slice","@stdlib/slice/base/str2multislice"
diff --git a/lib/node_modules/@stdlib/namespace/pkg2related/data/data.json b/lib/node_modules/@stdlib/namespace/pkg2related/data/data.json
index d62413bb0c99..e2e17c9e43c9 100644
--- a/lib/node_modules/@stdlib/namespace/pkg2related/data/data.json
+++ b/lib/node_modules/@stdlib/namespace/pkg2related/data/data.json
@@ -1 +1 @@
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diff --git a/lib/node_modules/@stdlib/namespace/pkg2standalone/data/data.csv b/lib/node_modules/@stdlib/namespace/pkg2standalone/data/data.csv
index 2be5976d956f..2dcbd3700b13 100644
--- a/lib/node_modules/@stdlib/namespace/pkg2standalone/data/data.csv
+++ b/lib/node_modules/@stdlib/namespace/pkg2standalone/data/data.csv
@@ -1430,12 +1430,12 @@
"@stdlib/blas/ext/base/ssumors","@stdlib/blas-ext-base-ssumors"
"@stdlib/blas/ext/base/ssumpw","@stdlib/blas-ext-base-ssumpw"
"@stdlib/blas/base/sswap","@stdlib/blas-base-sswap"
-"@stdlib/stats/base/stdev","@stdlib/stats-base-stdev"
-"@stdlib/stats/base/stdevch","@stdlib/stats-base-stdevch"
-"@stdlib/stats/base/stdevpn","@stdlib/stats-base-stdevpn"
-"@stdlib/stats/base/stdevtk","@stdlib/stats-base-stdevtk"
-"@stdlib/stats/base/stdevwd","@stdlib/stats-base-stdevwd"
-"@stdlib/stats/base/stdevyc","@stdlib/stats-base-stdevyc"
+"@stdlib/stats/strided/stdev","@stdlib/stats-strided-stdev"
+"@stdlib/stats/strided/stdevch","@stdlib/stats-strided-stdevch"
+"@stdlib/stats/strided/stdevpn","@stdlib/stats-strided-stdevpn"
+"@stdlib/stats/strided/stdevtk","@stdlib/stats-strided-stdevtk"
+"@stdlib/stats/strided/stdevwd","@stdlib/stats-strided-stdevwd"
+"@stdlib/stats/strided/stdevyc","@stdlib/stats-strided-stdevyc"
"@stdlib/math/strided/special/strunc","@stdlib/math-strided-special-strunc"
"@stdlib/stats/strided/svariance","@stdlib/stats-strided-svariance"
"@stdlib/stats/strided/svariancech","@stdlib/stats-strided-svariancech"
diff --git a/lib/node_modules/@stdlib/namespace/pkg2standalone/data/data.json b/lib/node_modules/@stdlib/namespace/pkg2standalone/data/data.json
index 4b0c9265e9af..acda97953291 100644
--- a/lib/node_modules/@stdlib/namespace/pkg2standalone/data/data.json
+++ b/lib/node_modules/@stdlib/namespace/pkg2standalone/data/data.json
@@ -1 +1 @@
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startcase":"@stdlib/string-startcase","@stdlib/string/starts-with":"@stdlib/string-starts-with","@stdlib/datasets/stopwords-en":"@stdlib/datasets-stopwords-en","@stdlib/math/strided/special/abs":"@stdlib/math-strided-special-abs","@stdlib/math/strided/special/abs2":"@stdlib/math-strided-special-abs2","@stdlib/math/strided/special/abs2-by":"@stdlib/math-strided-special-abs2-by","@stdlib/math/strided/special/abs-by":"@stdlib/math-strided-special-abs-by","@stdlib/math/strided/special/acos-by":"@stdlib/math-strided-special-acos-by","@stdlib/math/strided/special/acosh-by":"@stdlib/math-strided-special-acosh-by","@stdlib/math/strided/special/acot-by":"@stdlib/math-strided-special-acot-by","@stdlib/math/strided/special/acoth-by":"@stdlib/math-strided-special-acoth-by","@stdlib/math/strided/special/acovercos-by":"@stdlib/math-strided-special-acovercos-by","@stdlib/math/strided/special/acoversin-by":"@stdlib/math-strided-special-acoversin-by","@stdlib/math/strided/ops/add":"@stdlib/math-strided-ops-add","@stdlib/math/strided/ops/add-by":"@stdlib/math-strided-ops-add-by","@stdlib/math/strided/special/ahavercos-by":"@stdlib/math-strided-special-ahavercos-by","@stdlib/math/strided/special/ahaversin-by":"@stdlib/math-strided-special-ahaversin-by","@stdlib/math/strided/special/asin-by":"@stdlib/math-strided-special-asin-by","@stdlib/math/strided/special/asinh-by":"@stdlib/math-strided-special-asinh-by","@stdlib/math/strided/special/atan-by":"@stdlib/math-strided-special-atan-by","@stdlib/math/strided/special/atanh-by":"@stdlib/math-strided-special-atanh-by","@stdlib/math/strided/special/avercos-by":"@stdlib/math-strided-special-avercos-by","@stdlib/math/strided/special/aversin-by":"@stdlib/math-strided-special-aversin-by","@stdlib/math/strided/special/besselj0-by":"@stdlib/math-strided-special-besselj0-by","@stdlib/math/strided/special/besselj1-by":"@stdlib/math-strided-special-besselj1-by","@stdlib/math/strided/special/bessely0-by":"@stdlib/math-strided-special-bessely0-by","@stdlib/math/strided/special/bessely1-by":"@stdlib/math-strided-special-bessely1-by","@stdlib/math/strided/special/binet-by":"@stdlib/math-strided-special-binet-by","@stdlib/math/strided/special/cbrt":"@stdlib/math-strided-special-cbrt","@stdlib/math/strided/special/cbrt-by":"@stdlib/math-strided-special-cbrt-by","@stdlib/math/strided/special/ceil":"@stdlib/math-strided-special-ceil","@stdlib/math/strided/special/cos-by":"@stdlib/math-strided-special-cos-by","@stdlib/math/strided/special/deg2rad":"@stdlib/math-strided-special-deg2rad","@stdlib/strided/dtypes":"@stdlib/strided-dtypes","@stdlib/math/strided/special/dcbrt-by":"@stdlib/math-strided-special-dcbrt-by","@stdlib/strided/dispatch":"@stdlib/strided-dispatch","@stdlib/strided/dispatch-by":"@stdlib/strided-dispatch-by","@stdlib/math/strided/special/floor":"@stdlib/math-strided-special-floor","@stdlib/math/strided/special/inv":"@stdlib/math-strided-special-inv","@stdlib/math/strided/ops/mul":"@stdlib/math-strided-ops-mul","@stdlib/math/strided/ops/mul-by":"@stdlib/math-strided-ops-mul-by","@stdlib/math/strided/special/ramp":"@stdlib/math-strided-special-ramp","@stdlib/math/strided/special/rsqrt":"@stdlib/math-strided-special-rsqrt","@stdlib/math/strided/special/sin-by":"@stdlib/math-strided-special-sin-by","@stdlib/math/strided/special/sqrt":"@stdlib/math-strided-special-sqrt","@stdlib/math/strided/special/sqrt-by":"@stdlib/math-strided-special-sqrt-by","@stdlib/math/strided/ops/sub":"@stdlib/math-strided-ops-sub","@stdlib/math/strided/ops/sub-by":"@stdlib/math-strided-ops-sub-by","@stdlib/math/strided/special/trunc":"@stdlib/math-strided-special-trunc","@stdlib/array/to-strided-iterator":"@stdlib/array-to-strided-iterator","@stdlib/streams/node/from-strided-array":"@stdlib/streams-node-from-strided-array","@stdlib/buffer/from-string":"@stdlib/buffer-from-string","@stdlib/dstructs/struct":"@stdlib/dstructs-struct","@stdlib/ndarray/sub2ind":"@stdlib/ndarray-sub2ind","@stdlib/string/substring-after":"@stdlib/string-substring-after","@stdlib/string/substring-after-last":"@stdlib/string-substring-after-last","@stdlib/string/substring-before":"@stdlib/string-substring-before","@stdlib/string/substring-before-last":"@stdlib/string-substring-before-last","@stdlib/datasets/suthaharan-multi-hop-sensor-network":"@stdlib/datasets-suthaharan-multi-hop-sensor-network","@stdlib/datasets/suthaharan-single-hop-sensor-network":"@stdlib/datasets-suthaharan-single-hop-sensor-network","@stdlib/symbol/ctor":"@stdlib/symbol-ctor","@stdlib/utils/tabulate":"@stdlib/utils-tabulate","@stdlib/utils/tabulate-by":"@stdlib/utils-tabulate-by","@stdlib/utils/async/tabulate-by":"@stdlib/utils-async-tabulate-by","@stdlib/function/thunk":"@stdlib/function-thunk","@stdlib/time/tic":"@stdlib/time-tic","@stdlib/utils/timeit":"@stdlib/utils-timeit","@stdlib/os/tmpdir":"@stdlib/os-tmpdir","@stdlib/time/toc":"@stdlib/time-toc","@stdlib/nlp/tokenize":"@stdlib/nlp-tokenize","@stdlib/streams/node/transform":"@stdlib/streams-node-transform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diff --git a/lib/node_modules/@stdlib/namespace/standalone2pkg/data/data.csv b/lib/node_modules/@stdlib/namespace/standalone2pkg/data/data.csv
index 8cba695fc5ff..561ee826bc34 100644
--- a/lib/node_modules/@stdlib/namespace/standalone2pkg/data/data.csv
+++ b/lib/node_modules/@stdlib/namespace/standalone2pkg/data/data.csv
@@ -1430,12 +1430,12 @@
"@stdlib/blas-ext-base-ssumors","@stdlib/blas/ext/base/ssumors"
"@stdlib/blas-ext-base-ssumpw","@stdlib/blas/ext/base/ssumpw"
"@stdlib/blas-base-sswap","@stdlib/blas/base/sswap"
-"@stdlib/stats-base-stdev","@stdlib/stats/base/stdev"
-"@stdlib/stats-base-stdevch","@stdlib/stats/base/stdevch"
-"@stdlib/stats-base-stdevpn","@stdlib/stats/base/stdevpn"
-"@stdlib/stats-base-stdevtk","@stdlib/stats/base/stdevtk"
-"@stdlib/stats-base-stdevwd","@stdlib/stats/base/stdevwd"
-"@stdlib/stats-base-stdevyc","@stdlib/stats/base/stdevyc"
+"@stdlib/stats-strided-stdev","@stdlib/stats/strided/stdev"
+"@stdlib/stats-strided-stdevch","@stdlib/stats/strided/stdevch"
+"@stdlib/stats-strided-stdevpn","@stdlib/stats/strided/stdevpn"
+"@stdlib/stats-strided-stdevtk","@stdlib/stats/strided/stdevtk"
+"@stdlib/stats-strided-stdevwd","@stdlib/stats/strided/stdevwd"
+"@stdlib/stats-strided-stdevyc","@stdlib/stats/strided/stdevyc"
"@stdlib/math-strided-special-strunc","@stdlib/math/strided/special/strunc"
"@stdlib/stats-strided-svariance","@stdlib/stats/strided/svariance"
"@stdlib/stats-strided-svariancech","@stdlib/stats/strided/svariancech"
diff --git a/lib/node_modules/@stdlib/namespace/standalone2pkg/data/data.json b/lib/node_modules/@stdlib/namespace/standalone2pkg/data/data.json
index cc1f6d2ceca4..28228bf8dea4 100644
--- a/lib/node_modules/@stdlib/namespace/standalone2pkg/data/data.json
+++ b/lib/node_modules/@stdlib/namespace/standalone2pkg/data/data.json
@@ -1 +1 @@
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diff --git a/lib/node_modules/@stdlib/repl/help/data/data.csv b/lib/node_modules/@stdlib/repl/help/data/data.csv
index d531d3e7bdbe..18d52ceb74dc 100644
--- a/lib/node_modules/@stdlib/repl/help/data/data.csv
+++ b/lib/node_modules/@stdlib/repl/help/data/data.csv
@@ -1085,7 +1085,7 @@ base.kernelBetainc,"\nbase.kernelBetainc( x, a, b, regularized, upper )\n Com
base.kernelBetainc.assign,"\nbase.kernelBetainc.assign( x, a, b, regularized, upper, out, stride, offset )\n Computes the kernel function for the regularized incomplete beta function.\n\n The `regularized` and `upper` parameters specify whether to evaluate the\n non-regularized and/or upper incomplete beta functions, respectively.\n\n If provided `x < 0` or `x > 1`, the function returns `[ NaN, NaN ]`.\n\n If provided `a < 0` or `b < 0`, the function returns `[ NaN, NaN ]`.\n\n If provided `NaN` for `x`, `a`, or `b`, the function returns `[ NaN, NaN ]`.\n\n Parameters\n ----------\n x: number\n First function parameter.\n\n a: number\n Second function parameter.\n\n b: number\n Third function parameter.\n\n regularized: boolean\n Boolean indicating whether the function should evaluate the regularized\n or non-regularized incomplete beta function.\n\n upper: boolean\n Boolean indicating whether the function should return the upper tail of\n the incomplete beta function.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n y: Array|TypedArray|Object\n Function value and first derivative.\n\n Examples\n --------\n > var out = [ 0.0, 0.0 ];\n > var v = base.kernelBetainc.assign( 0.2, 1.0, 2.0, true, true, out, 1, 0 )\n [ 0.64, 1.6 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.betainc"
base.kernelBetaincinv,"\nbase.kernelBetaincinv( a, b, p, q )\n Computes the inverse of the lower incomplete beta function.\n\n Probabilities `p` and `q` must satisfy `p = 1 - q`.\n\n Parameters\n ----------\n a: number\n First function parameter (a positive number).\n\n b: number\n Second function parameter (a positive number).\n\n p: number\n Probability.\n\n q: number\n Probability equal to `1-p`.\n\n Returns\n -------\n out: Array\n Two-element array holding function value `y` and `1-y`.\n\n Examples\n --------\n > var y = base.kernelBetaincinv( 3.0, 3.0, 0.2, 0.8 )\n [ ~0.327, ~0.673 ]\n > y = base.kernelBetaincinv( 3.0, 3.0, 0.4, 0.6 )\n [ ~0.446, ~0.554 ]\n > y = base.kernelBetaincinv( 1.0, 6.0, 0.4, 0.6 )\n [ ~0.082, ~0.918 ]\n > y = base.kernelBetaincinv( 1.0, 6.0, 0.8, 0.2 )\n [ ~0.235, ~0.765 ]\n\n See Also\n --------\n base.betaincinv\n"
base.kernelCos,"\nbase.kernelCos( x, y )\n Computes the cosine of a double-precision floating-point number on the\n interval [-π/4, π/4].\n\n For increased accuracy, the number for which the cosine should be evaluated\n can be supplied as a double-double number (i.e., a non-evaluated sum of two\n double-precision floating-point numbers `x` and `y`).\n\n The two numbers must satisfy `|y| < 0.5 * ulp( x )`.\n\n If either argument is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n y: number\n Tail of `x`.\n\n Returns\n -------\n out: number\n Cosine.\n\n Examples\n --------\n > var out = base.kernelCos( 0.0, 0.0 )\n ~1.0\n > out = base.kernelCos( PI/6.0, 0.0 )\n ~0.866\n > out = base.kernelCos( 0.785, -1.144e-17 )\n ~0.707\n > out = base.kernelCos( NaN )\n NaN\n\n See Also\n --------\n base.cos, base.kernelSin, base.kernelTan\n"
-base.kernelLog1p,"\nbase.kernelLog1p( f )\n Computes `log(1+f) - f` for `1+f` in ~[sqrt(2)/2, sqrt(2)].\n\n This function provides a common means for computing logarithms in base e.\n Argument reduction and adding the final term of the polynomial must be done\n by the caller for increased accuracy when different bases are used.\n\n Parameters\n ----------\n f: number\n Input value.\n\n Returns\n -------\n out: number\n Function value.\n\n Examples\n --------\n > var y = base.kernelLog1p( 1.0 )\n ~0.1931\n > y = base.kernelLog1p( 1.4142135623730951 )\n ~0.4672\n > y = base.kernelLog1p( NaN )\n NaN\n\n See Also\n --------\n base.log1p\n"
+base.kernelLog1p,"\nbase.kernelLog1p( f )\n Evaluates a correction term for double-precision base-2 and base-10\n logarithms when `1+f` is in `[√2/2, √2]`.\n\n This function provides a common means for computing logarithms in base e.\n Argument reduction and adding the final term of the polynomial must be done\n by the caller for increased accuracy when different bases are used.\n\n Parameters\n ----------\n f: number\n Input value.\n\n Returns\n -------\n out: number\n Function value.\n\n Examples\n --------\n > var y = base.kernelLog1p( 1.0 )\n ~0.1931\n > y = base.kernelLog1p( 1.4142135623730951 )\n ~0.4672\n > y = base.kernelLog1p( NaN )\n NaN\n\n See Also\n --------\n base.log1p\n"
base.kernelSin,"\nbase.kernelSin( x, y )\n Computes the sine of a double-precision floating-point number on [-π/4,π/4].\n\n For increased accuracy, the number for which the cosine should be evaluated\n can be supplied as a double-double number (i.e., a non-evaluated sum of two\n double-precision floating-point numbers `x` and `y`).\n\n The two numbers must satisfy `|y| < 0.5 * ulp( x )`.\n\n If either argument is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n y: number\n Tail of `x`.\n\n Returns\n -------\n out: number\n Sine.\n\n Examples\n --------\n > var y = base.kernelSin( 0.0, 0.0 )\n ~0.0\n > y = base.kernelSin( PI/6.0, 0.0 )\n ~0.5\n > y = base.kernelSin( 0.619, 9.279e-18 )\n ~0.58\n\n > y = base.kernelSin( NaN, 0.0 )\n NaN\n > y = base.kernelSin( 2.0, NaN )\n NaN\n > y = base.kernelSin( NaN, NaN )\n NaN\n\n See Also\n --------\n base.kernelCos, base.kernelTan, base.sin\n"
base.kernelTan,"\nbase.kernelTan( x, y, k )\n Computes the tangent of a double-precision floating-point number on the\n interval [-π/4, π/4].\n\n For increased accuracy, the number for which the tangent should be evaluated\n can be supplied as a double-double number (i.e., a non-evaluated sum of two\n double-precision floating-point numbers `x` and `y`).\n\n The numbers `x` and `y` must satisfy `|y| < 0.5 * ulp( x )`.\n\n If either `x` or `y` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n y: number\n Tail of `x`.\n\n k: integer\n If `k=1`, the function returns `tan(x+y)`. If `k=-1`, the function\n returns the negative inverse `-1/tan(x+y)`.\n\n Returns\n -------\n out: number\n Tangent.\n\n Examples\n --------\n > var out = base.kernelTan( PI/4.0, 0.0, 1 )\n ~1.0\n > out = base.kernelTan( PI/4.0, 0.0, -1 )\n ~-1.0\n > out = base.kernelTan( PI/6.0, 0.0, 1 )\n ~0.577\n > out = base.kernelTan( 0.664, 5.288e-17, 1 )\n ~0.783\n\n > out = base.kernelTan( NaN, 0.0, 1 )\n NaN\n > out = base.kernelTan( 3.0, NaN, 1 )\n NaN\n > out = base.kernelTan( 3.0, 0.0, NaN )\n NaN\n\n See Also\n --------\n base.kernelCos, base.kernelSin, base.tan\n"
base.kroneckerDelta,"\nbase.kroneckerDelta( i, j )\n Evaluates the Kronecker delta.\n\n If `i == j`, the function returns `1`; otherwise, the function returns zero.\n\n Parameters\n ----------\n i: number\n Input value.\n\n j: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.kroneckerDelta( 3.14, 0.0 )\n 0.0\n > y = base.kroneckerDelta( 3.14, 3.14 )\n 1.0\n\n See Also\n --------\n base.diracDelta\n"
@@ -1967,18 +1967,18 @@ base.strided.dssumors,"\nbase.strided.dssumors( N, x, strideX )\n Computes th
base.strided.dssumors.ndarray,"\nbase.strided.dssumors.ndarray( N, x, strideX, offsetX )\n Computes the sum of single-precision floating-point strided array elements\n using ordinary recursive summation with extended accumulation and\n alternative indexing semantics and returning an extended precision result.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dssumors.ndarray( x.length, x, 1, 0 )\n 1.0\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dssumors.ndarray( 3, x, 2, 1 )\n -1.0\n\n See Also\n --------\n base.strided.dsnansumors, base.strided.dssum, base.strided.dsumors, base.strided.ssumors"
base.strided.dssumpw,"\nbase.strided.dssumpw( N, x, strideX )\n Computes the sum of single-precision floating-point strided array elements\n using pairwise summation with extended accumulation and returning an\n extended precision result.\n\n The `N` and stride parameters determine which elements in `x` are accessed\n at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `0.0`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dssumpw( x.length, x, 1 )\n 1.0\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dssumpw( 3, x, 2 )\n 1.0\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dssumpw( 3, x1, 2 )\n -1.0\n\n\nbase.strided.dssumpw.ndarray( N, x, strideX, offsetX )\n Computes the sum of single-precision floating-point strided array elements\n using pairwise summation with extended accumulation and alternative indexing\n semantics and returning an extended precision result.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dssumpw.ndarray( x.length, x, 1, 0 )\n 1.0\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dssumpw.ndarray( 3, x, 2, 1 )\n -1.0\n\n See Also\n --------\n base.strided.dsnansumpw, base.strided.dssum, base.strided.dsumpw, base.strided.ssumpw\n"
base.strided.dssumpw.ndarray,"\nbase.strided.dssumpw.ndarray( N, x, strideX, offsetX )\n Computes the sum of single-precision floating-point strided array elements\n using pairwise summation with extended accumulation and alternative indexing\n semantics and returning an extended precision result.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dssumpw.ndarray( x.length, x, 1, 0 )\n 1.0\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dssumpw.ndarray( 3, x, 2, 1 )\n -1.0\n\n See Also\n --------\n base.strided.dsnansumpw, base.strided.dssum, base.strided.dsumpw, base.strided.ssumpw"
-base.strided.dstdev,"\nbase.strided.dstdev( N, correction, x, strideX )\n Computes the standard deviation of a double-precision floating-point strided\n array.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdev( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dstdev( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dstdev( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.dstdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdev.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdev, base.strided.dvariance, base.strided.sstdev, base.strided.stdev\n"
-base.strided.dstdev.ndarray,"\nbase.strided.dstdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdev.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdev, base.strided.dvariance, base.strided.sstdev, base.strided.stdev"
-base.strided.dstdevch,"\nbase.strided.dstdevch( N, correction, x, strideX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass trial mean algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevch( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dstdevch( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dstdevch( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.dstdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass trial mean algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevch.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevch, base.strided.dstdev, base.strided.dvariancech, base.strided.sstdevch, base.strided.stdevch\n"
-base.strided.dstdevch.ndarray,"\nbase.strided.dstdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass trial mean algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevch.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevch, base.strided.dstdev, base.strided.dvariancech, base.strided.sstdevch, base.strided.stdevch"
-base.strided.dstdevpn,"\nbase.strided.dstdevpn( N, correction, x, strideX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a two-pass algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride Length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevpn( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dstdevpn( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dstdevpn( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.dstdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a two-pass algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride Length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevpn.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevpn, base.strided.dstdev, base.strided.dvariancepn, base.strided.sstdevpn, base.strided.stdevpn\n"
-base.strided.dstdevpn.ndarray,"\nbase.strided.dstdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a two-pass algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride Length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevpn.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevpn, base.strided.dstdev, base.strided.dvariancepn, base.strided.sstdevpn, base.strided.stdevpn"
-base.strided.dstdevtk,"\nbase.strided.dstdevtk( N, correction, x, strideX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass textbook algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevtk( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dstdevtk( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dstdevtk( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.dstdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass textbook algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevtk.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevtk, base.strided.dstdev, base.strided.dvariancetk, base.strided.sstdevtk, base.strided.stdevtk\n"
-base.strided.dstdevtk.ndarray,"\nbase.strided.dstdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass textbook algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevtk.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevtk, base.strided.dstdev, base.strided.dvariancetk, base.strided.sstdevtk, base.strided.stdevtk"
-base.strided.dstdevwd,"\nbase.strided.dstdevwd( N, correction, x, strideX )\n Computes the standard deviation of a double-precision floating-point strided\n array using Welford's algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevwd( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dstdevwd( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dstdevwd( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.dstdevwd.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using Welford's algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevwd.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevwd, base.strided.dstdev, base.strided.dvariancewd, base.strided.sstdevwd, base.strided.stdevwd\n"
-base.strided.dstdevwd.ndarray,"\nbase.strided.dstdevwd.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using Welford's algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevwd.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevwd, base.strided.dstdev, base.strided.dvariancewd, base.strided.sstdevwd, base.strided.stdevwd"
-base.strided.dstdevyc,"\nbase.strided.dstdevyc( N, correction, x, strideX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass algorithm proposed by Youngs and Cramer.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevyc( x.length, 1.0, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dstdevyc( 3, 1.0, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dstdevyc( 3, 1.0, x1, 2 )\n ~2.0817\n\n\nbase.strided.dstdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass algorithm proposed by Youngs and Cramer and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevyc.ndarray( x.length, 1.0, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevyc.ndarray( 3, 1.0, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevyc, base.strided.dstdev, base.strided.dvarianceyc, base.strided.sstdevyc, base.strided.stdevyc\n"
-base.strided.dstdevyc.ndarray,"\nbase.strided.dstdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass algorithm proposed by Youngs and Cramer and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevyc.ndarray( x.length, 1.0, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevyc.ndarray( 3, 1.0, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevyc, base.strided.dstdev, base.strided.dvarianceyc, base.strided.sstdevyc, base.strided.stdevyc"
+base.strided.dstdev,"\nbase.strided.dstdev( N, correction, x, strideX )\n Computes the standard deviation of a double-precision floating-point strided\n array.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdev( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dstdev( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dstdev( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.dstdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdev.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdev, base.strided.dvariance, base.strided.sstdev\n"
+base.strided.dstdev.ndarray,"\nbase.strided.dstdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdev.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdev, base.strided.dvariance, base.strided.sstdev"
+base.strided.dstdevch,"\nbase.strided.dstdevch( N, correction, x, strideX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass trial mean algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevch( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dstdevch( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dstdevch( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.dstdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass trial mean algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevch.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevch, base.strided.dstdev, base.strided.dvariancech, base.strided.sstdevch\n"
+base.strided.dstdevch.ndarray,"\nbase.strided.dstdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass trial mean algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevch.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevch, base.strided.dstdev, base.strided.dvariancech, base.strided.sstdevch"
+base.strided.dstdevpn,"\nbase.strided.dstdevpn( N, correction, x, strideX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a two-pass algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride Length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevpn( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dstdevpn( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dstdevpn( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.dstdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a two-pass algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride Length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevpn.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevpn, base.strided.dstdev, base.strided.dvariancepn, base.strided.sstdevpn\n"
+base.strided.dstdevpn.ndarray,"\nbase.strided.dstdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a two-pass algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride Length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevpn.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevpn, base.strided.dstdev, base.strided.dvariancepn, base.strided.sstdevpn"
+base.strided.dstdevtk,"\nbase.strided.dstdevtk( N, correction, x, strideX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass textbook algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevtk( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dstdevtk( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dstdevtk( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.dstdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass textbook algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevtk.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevtk, base.strided.dstdev, base.strided.dvariancetk, base.strided.sstdevtk\n"
+base.strided.dstdevtk.ndarray,"\nbase.strided.dstdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass textbook algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevtk.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevtk, base.strided.dstdev, base.strided.dvariancetk, base.strided.sstdevtk"
+base.strided.dstdevwd,"\nbase.strided.dstdevwd( N, correction, x, strideX )\n Computes the standard deviation of a double-precision floating-point strided\n array using Welford's algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevwd( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dstdevwd( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dstdevwd( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.dstdevwd.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using Welford's algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevwd.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevwd, base.strided.dstdev, base.strided.dvariancewd, base.strided.sstdevwd\n"
+base.strided.dstdevwd.ndarray,"\nbase.strided.dstdevwd.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using Welford's algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevwd.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevwd, base.strided.dstdev, base.strided.dvariancewd, base.strided.sstdevwd"
+base.strided.dstdevyc,"\nbase.strided.dstdevyc( N, correction, x, strideX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass algorithm proposed by Youngs and Cramer.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevyc( x.length, 1.0, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dstdevyc( 3, 1.0, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dstdevyc( 3, 1.0, x1, 2 )\n ~2.0817\n\n\nbase.strided.dstdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass algorithm proposed by Youngs and Cramer and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevyc.ndarray( x.length, 1.0, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevyc.ndarray( 3, 1.0, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevyc, base.strided.dstdev, base.strided.dvarianceyc, base.strided.sstdevyc\n"
+base.strided.dstdevyc.ndarray,"\nbase.strided.dstdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a double-precision floating-point strided\n array using a one-pass algorithm proposed by Youngs and Cramer and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dstdevyc.ndarray( x.length, 1.0, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dstdevyc.ndarray( 3, 1.0, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevyc, base.strided.dstdev, base.strided.dvarianceyc, base.strided.sstdevyc"
base.strided.dsum,"\nbase.strided.dsum( N, x, strideX )\n Computes the sum of double-precision floating-point strided array elements.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `0.0`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dsum( x.length, x, 1 )\n 1.0\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dsum( 3, x, 2 )\n 1.0\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dsum( 3, x1, 2 )\n -1.0\n\n\nbase.strided.dsum.ndarray( N, x, strideX, offsetX )\n Computes the sum of double-precision floating-point strided array elements\n using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dsum.ndarray( x.length, x, 1, 0 )\n 1.0\n\n // Using offset parameter:\n > x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dsum.ndarray( 3, x, 2, 1 )\n -1.0\n\n See Also\n --------\n base.strided.dasum, base.strided.dmean, base.strided.dnansum, base.strided.ssum, base.strided.gsum\n"
base.strided.dsum.ndarray,"\nbase.strided.dsum.ndarray( N, x, strideX, offsetX )\n Computes the sum of double-precision floating-point strided array elements\n using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dsum.ndarray( x.length, x, 1, 0 )\n 1.0\n\n // Using offset parameter:\n > x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dsum.ndarray( 3, x, 2, 1 )\n -1.0\n\n See Also\n --------\n base.strided.dasum, base.strided.dmean, base.strided.dnansum, base.strided.ssum, base.strided.gsum"
base.strided.dsumkbn,"\nbase.strided.dsumkbn( N, x, strideX )\n Computes the sum of double-precision floating-point strided array elements\n using an improved Kahan–Babuška algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `0.0`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dsumkbn( x.length, x, 1 )\n 1.0\n\n // Using `N` and stride parameters:\n > x = new Float64Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.dsumkbn( 3, x, 2 )\n 1.0\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.dsumkbn( 3, x1, 2 )\n -1.0\n\n\nbase.strided.dsumkbn.ndarray( N, x, strideX, offsetX )\n Computes the sum of double-precision floating-point strided array elements\n using an improved Kahan–Babuška algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float64Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.dsumkbn.ndarray( x.length, x, 1, 0 )\n 1.0\n\n // Using offset parameter:\n > var x = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.dsumkbn.ndarray( 3, x, 2, 1 )\n -1.0\n\n See Also\n --------\n base.strided.dnansumkbn, base.strided.dsum, base.strided.dsumkbn2, base.strided.dsumors, base.strided.dsumpw, base.strided.gsumkbn, base.strided.ssumkbn\n"
@@ -2176,18 +2176,18 @@ base.strided.nanrange,"\nbase.strided.nanrange( N, x, strideX )\n Computes th
base.strided.nanrange.ndarray,"\nbase.strided.nanrange.ndarray( N, x, strideX, offsetX )\n Computes the range of a strided array, ignoring `NaN` values and using\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n Range.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanrange.ndarray( x.length, x, 1, 0 )\n 4.0\n\n // Using offset parameter:\n > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanrange.ndarray( 4, x, 2, 1 )\n 4.0\n\n See Also\n --------\n base.strided.dnanrange, base.strided.nanmax, base.strided.nanmin, base.strided.range, base.strided.snanrange"
base.strided.nanrangeBy,"\nbase.strided.nanrangeBy( N, x, strideX, clbk[, thisArg] )\n Computes the range of a strided array via a callback function, ignoring\n `NaN` values.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use typed\n array views.\n\n If `N <= 0`, the function returns `x` unchanged.\n\n The callback function is provided four arguments:\n\n - value: array element.\n - aidx: array index.\n - sidx: strided index (offset + aidx*stride).\n - array: the input array.\n\n The callback function should return a numeric value.\n\n If the callback function returns `NaN`, the value is ignored.\n\n If the callback function does not return any value (or equivalently,\n explicitly returns `undefined`), the value is ignored.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Array|TypedArray|Object\n Input array/collection. If provided an object, the object must be array-\n like (excluding strings and functions).\n\n strideX: integer\n Stride length.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n out: number\n Range.\n\n Examples\n --------\n // Standard Usage:\n > function accessor( v ) { return v * 2.0; };\n > var x = [ -2.0, 1.0, 3.0, -5.0, 4.0, NaN, -1.0, -3.0 ];\n > base.strided.nanrangeBy( x.length, x, 1, accessor )\n 18.0\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 3.0, -5.0, 4.0, -1.0, -3.0, 1.0 ];\n > base.strided.nanrangeBy( 4, x, 2, accessor )\n 14.0\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanrangeBy( 3, x1, 2, accessor )\n 8.0\n\n\nbase.strided.nanrangeBy.ndarray( N, x, strideX, offsetX, clbk[, thisArg] )\n Calculates the range of a strided array via a callback function, ignoring\n `NaN` values and using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Array|TypedArray|Object\n Input array/collection. If provided an object, the object must be array-\n like (excluding strings and functions).\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n out: number\n Range.\n\n Examples\n --------\n // Standard Usage:\n > function accessor( v ) { return v * 2.0; };\n > var x = [ -2.0, 1.0, 3.0, -5.0, 4.0, NaN, -1.0, -3.0 ];\n > base.strided.nanrangeBy.ndarray( x.length, x, 1, 0, accessor )\n 18.0\n\n // Using an index offset:\n > x = [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ];\n > base.strided.nanrangeBy.ndarray( 3, x, 2, 1, accessor )\n 8.0\n\n See Also\n --------\n base.strided.dnanrange, base.strided.nanmaxBy, base.strided.nanminBy, base.strided.nanrange, base.strided.rangeBy, base.strided.snanrange\n"
base.strided.nanrangeBy.ndarray,"\nbase.strided.nanrangeBy.ndarray( N, x, strideX, offsetX, clbk[, thisArg] )\n Calculates the range of a strided array via a callback function, ignoring\n `NaN` values and using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Array|TypedArray|Object\n Input array/collection. If provided an object, the object must be array-\n like (excluding strings and functions).\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n out: number\n Range.\n\n Examples\n --------\n // Standard Usage:\n > function accessor( v ) { return v * 2.0; };\n > var x = [ -2.0, 1.0, 3.0, -5.0, 4.0, NaN, -1.0, -3.0 ];\n > base.strided.nanrangeBy.ndarray( x.length, x, 1, 0, accessor )\n 18.0\n\n // Using an index offset:\n > x = [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ];\n > base.strided.nanrangeBy.ndarray( 3, x, 2, 1, accessor )\n 8.0\n\n See Also\n --------\n base.strided.dnanrange, base.strided.nanmaxBy, base.strided.nanminBy, base.strided.nanrange, base.strided.rangeBy, base.strided.snanrange"
-base.strided.nanstdev,"\nbase.strided.nanstdev( N, correction, x, strideX )\n Computes the standard deviation of a strided array ignoring `NaN` values.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdev( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanstdev( 4, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0,NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanstdev( 4, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.nanstdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdev.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdev, base.strided.nanvariance, base.strided.snanstdev, base.strided.stdev\n"
-base.strided.nanstdev.ndarray,"\nbase.strided.nanstdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdev.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdev, base.strided.nanvariance, base.strided.snanstdev, base.strided.stdev"
-base.strided.nanstdevch,"\nbase.strided.nanstdevch( N, correction, x, strideX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass trial mean algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevch( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanstdevch( 4, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanstdevch( 4, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.nanstdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass trial mean algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevch.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevch, base.strided.nanvariancech, base.strided.nanstdev, base.strided.snanstdevch, base.strided.stdevch\n"
-base.strided.nanstdevch.ndarray,"\nbase.strided.nanstdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass trial mean algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevch.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevch, base.strided.nanvariancech, base.strided.nanstdev, base.strided.snanstdevch, base.strided.stdevch"
-base.strided.nanstdevpn,"\nbase.strided.nanstdevpn( N, correction, x, strideX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a two-pass algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevpn( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanstdevpn( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanstdevpn( 4, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.nanstdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a two-pass algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevpn.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevpn, base.strided.nanvariancepn, base.strided.nanstdev, base.strided.snanstdevpn, base.strided.stdevpn\n"
-base.strided.nanstdevpn.ndarray,"\nbase.strided.nanstdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a two-pass algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevpn.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevpn, base.strided.nanvariancepn, base.strided.nanstdev, base.strided.snanstdevpn, base.strided.stdevpn"
-base.strided.nanstdevtk,"\nbase.strided.nanstdevtk( N, correction, x, strideX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass textbook algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevtk( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanstdevtk( 4, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanstdevtk( 4, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.nanstdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass textbook algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevtk.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevtk, base.strided.nanvariancetk, base.strided.nanstdev, base.strided.snanstdevtk, base.strided.stdevtk\n"
-base.strided.nanstdevtk.ndarray,"\nbase.strided.nanstdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass textbook algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevtk.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevtk, base.strided.nanvariancetk, base.strided.nanstdev, base.strided.snanstdevtk, base.strided.stdevtk"
-base.strided.nanstdevwd,"\nbase.strided.nanstdevwd( N, correction, x, strideX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using Welford's algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevwd( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanstdevwd( 4, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanstdevwd( 4, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.nanstdevwd.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using Welford's algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevwd.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevwd, base.strided.nanvariancewd, base.strided.nanstdev, base.strided.snanstdevwd, base.strided.stdevwd\n"
-base.strided.nanstdevwd.ndarray,"\nbase.strided.nanstdevwd.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using Welford's algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevwd.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevwd, base.strided.nanvariancewd, base.strided.nanstdev, base.strided.snanstdevwd, base.strided.stdevwd"
-base.strided.nanstdevyc,"\nbase.strided.nanstdevyc( N, correction, x, strideX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass algorithm proposed by Youngs and Cramer.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevyc( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and `stride` parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanstdevyc( 4, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanstdevyc( 4, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.nanstdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass algorithm proposed by Youngs and Cramer and alternative\n indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevyc.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevyc.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevyc, base.strided.nanvarianceyc, base.strided.nanstdev, base.strided.snanstdevyc, base.strided.stdevyc\n"
-base.strided.nanstdevyc.ndarray,"\nbase.strided.nanstdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass algorithm proposed by Youngs and Cramer and alternative\n indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevyc.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevyc.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevyc, base.strided.nanvarianceyc, base.strided.nanstdev, base.strided.snanstdevyc, base.strided.stdevyc"
+base.strided.nanstdev,"\nbase.strided.nanstdev( N, correction, x, strideX )\n Computes the standard deviation of a strided array ignoring `NaN` values.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdev( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanstdev( 4, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0,NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanstdev( 4, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.nanstdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdev.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdev, base.strided.nanvariance, base.strided.snanstdev\n"
+base.strided.nanstdev.ndarray,"\nbase.strided.nanstdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdev.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdev, base.strided.nanvariance, base.strided.snanstdev"
+base.strided.nanstdevch,"\nbase.strided.nanstdevch( N, correction, x, strideX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass trial mean algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevch( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanstdevch( 4, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanstdevch( 4, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.nanstdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass trial mean algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevch.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevch, base.strided.nanvariancech, base.strided.nanstdev, base.strided.snanstdevch\n"
+base.strided.nanstdevch.ndarray,"\nbase.strided.nanstdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass trial mean algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevch.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevch, base.strided.nanvariancech, base.strided.nanstdev, base.strided.snanstdevch"
+base.strided.nanstdevpn,"\nbase.strided.nanstdevpn( N, correction, x, strideX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a two-pass algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevpn( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanstdevpn( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanstdevpn( 4, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.nanstdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a two-pass algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevpn.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevpn, base.strided.nanvariancepn, base.strided.nanstdev, base.strided.snanstdevpn\n"
+base.strided.nanstdevpn.ndarray,"\nbase.strided.nanstdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a two-pass algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevpn.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevpn, base.strided.nanvariancepn, base.strided.nanstdev, base.strided.snanstdevpn"
+base.strided.nanstdevtk,"\nbase.strided.nanstdevtk( N, correction, x, strideX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass textbook algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevtk( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanstdevtk( 4, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanstdevtk( 4, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.nanstdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass textbook algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevtk.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevtk, base.strided.nanvariancetk, base.strided.nanstdev, base.strided.snanstdevtk\n"
+base.strided.nanstdevtk.ndarray,"\nbase.strided.nanstdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass textbook algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevtk.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevtk, base.strided.nanvariancetk, base.strided.nanstdev, base.strided.snanstdevtk"
+base.strided.nanstdevwd,"\nbase.strided.nanstdevwd( N, correction, x, strideX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using Welford's algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevwd( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanstdevwd( 4, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanstdevwd( 4, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.nanstdevwd.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using Welford's algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevwd.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevwd, base.strided.nanvariancewd, base.strided.nanstdev, base.strided.snanstdevwd\n"
+base.strided.nanstdevwd.ndarray,"\nbase.strided.nanstdevwd.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using Welford's algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevwd.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevwd, base.strided.nanvariancewd, base.strided.nanstdev, base.strided.snanstdevwd"
+base.strided.nanstdevyc,"\nbase.strided.nanstdevyc( N, correction, x, strideX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass algorithm proposed by Youngs and Cramer.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevyc( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and `stride` parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanstdevyc( 4, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanstdevyc( 4, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.nanstdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass algorithm proposed by Youngs and Cramer and alternative\n indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevyc.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevyc.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevyc, base.strided.nanvarianceyc, base.strided.nanstdev, base.strided.snanstdevyc\n"
+base.strided.nanstdevyc.ndarray,"\nbase.strided.nanstdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array ignoring `NaN` values and\n using a one-pass algorithm proposed by Youngs and Cramer and alternative\n indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanstdevyc.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanstdevyc.ndarray( 4, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dnanstdevyc, base.strided.nanvarianceyc, base.strided.nanstdev, base.strided.snanstdevyc"
base.strided.nanvariance,"\nbase.strided.nanvariance( N, correction, x, strideX )\n Computes the variance of a strided array ignoring `NaN` values.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `n - c` where `c` corresponds to the\n provided degrees of freedom adjustment and `n` corresponds to the number\n of non-`NaN` indexed elements. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanvariance( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];\n > base.strided.nanvariance( 4, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanvariance( 4, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.nanvariance.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array ignoring `NaN` values and using\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `n - c` where `c` corresponds to the\n provided degrees of freedom adjustment and `n` corresponds to the number\n of non-`NaN` indexed elements. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanvariance.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanvariance.ndarray( 4, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dnanvariance, base.strided.nanstdev, base.strided.snanvariance, base.strided.variance\n"
base.strided.nanvariance.ndarray,"\nbase.strided.nanvariance.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array ignoring `NaN` values and using\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `n - c` where `c` corresponds to the\n provided degrees of freedom adjustment and `n` corresponds to the number\n of non-`NaN` indexed elements. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanvariance.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanvariance.ndarray( 4, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dnanvariance, base.strided.nanstdev, base.strided.snanvariance, base.strided.variance"
base.strided.nanvariancech,"\nbase.strided.nanvariancech( N, correction, x, strideX )\n Computes the variance of a strided array ignoring `NaN` values and using a\n one-pass trial mean algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n If every indexed element is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `n - c` where `c` corresponds to the\n provided degrees of freedom adjustment and `n` corresponds to the number\n of non-`NaN` indexed elements. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanvariancech( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN, NaN ];\n > base.strided.nanvariancech( 4, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.nanvariancech( 4, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.nanvariancech.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array ignoring `NaN` values and using a\n one-pass trial mean algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `n - c` where `c` corresponds to the\n provided degrees of freedom adjustment and `n` corresponds to the number\n of non-`NaN` indexed elements. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, NaN, 2.0 ];\n > base.strided.nanvariancech.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];\n > base.strided.nanvariancech.ndarray( 4, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dnanvariancech, base.strided.nanstdevch, base.strided.nanvariance, base.strided.snanvariancech, base.strided.variancech\n"
@@ -2448,18 +2448,18 @@ base.strided.ssortsh,"\nbase.strided.ssortsh( N, order, x, stride )\n Sorts a
base.strided.ssortsh.ndarray,"\nbase.strided.ssortsh.ndarray( N, order, x, stride, offset )\n Sorts a single-precision floating-point strided array using Shellsort and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n order: number\n Sort order. If `order < 0`, the function sorts `x` in decreasing order.\n If `order > 0`, the function sorts `x` in increasing order.\n\n x: Float32Array\n Input array.\n\n stride: integer\n Index increment for `x`.\n\n offset: integer\n Starting index of `x`.\n\n Returns\n -------\n x: Float32Array\n Input array `x`.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, -4.0 ] );\n > base.strided.ssortsh.ndarray( x.length, 1, x, 1, 0 )\n [ -4.0, -2.0, 1.0, 3.0 ]\n\n // Using an index offset:\n > x = new Float32Array( [ 1.0, -2.0, 3.0, -4.0 ] );\n > base.strided.ssortsh.ndarray( 2, 1, x, 2, 1 )\n [ 1.0, -4.0, 3.0, -2.0 ]\n\n See Also\n --------\n base.strided.dsortsh, base.strided.gsortsh, base.strided.ssort2sh"
base.strided.ssqrt,"\nbase.strided.ssqrt( N, x, strideX, y, strideY )\n Computes the principal square root for each element in a single-precision\n floating-point strided array `x` and assigns the results to elements in a\n single-precision floating-point strided array `y`.\n\n The `N` and `stride` parameters determine which elements in `x` and `y` are\n accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use typed\n array views.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Index increment for `x`.\n\n y: Float32Array\n Destination array.\n\n strideY: integer\n Index increment for `y`.\n\n Returns\n -------\n y: Float32Array\n Input array `y`.\n\n Examples\n --------\n // Standard usage:\n > var x = new Float32Array( [ 0.0, 4.0, 9.0, 12.0 ] );\n > var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > base.strided.ssqrt( x.length, x, 1, y, 1 )\n [ 0.0, 2.0, 3.0, ~3.464 ]\n\n // Using `N` and `stride` parameters:\n > var N = base.floor( x.length / 2 );\n > y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > base.strided.ssqrt( N, x, 2, y, -1 )\n [ 3.0, 0.0, 0.0, 0.0 ]\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 0.0, 4.0, 9.0, 12.0 ] );\n > var y0 = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > var y1 = new Float32Array( y0.buffer, y0.BYTES_PER_ELEMENT*2 );\n > N = base.floor( x0.length / 2 );\n > base.strided.ssqrt( N, x1, -2, y1, 1 )\n [ ~3.464, 2.0 ]\n > y0\n [ 0.0, 0.0, ~3.464, 2.0 ]\n\n\nbase.strided.ssqrt.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )\n Computes the principal square root for each element in a single-precision\n floating-point strided array `x` and assigns the results to elements in a\n single-precision floating-point strided array `y` using alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offsetX` and `offsetY` parameters support indexing semantics\n based on starting indices.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Index increment for `x`.\n\n offsetX: integer\n Starting index for `x`.\n\n y: Float32Array\n Destination array.\n\n strideY: integer\n Index increment for `y`.\n\n offsetY: integer\n Starting index for `y`.\n\n Returns\n -------\n y: Float32Array\n Input array `y`.\n\n Examples\n --------\n // Standard usage:\n > var x = new Float32Array( [ 0.0, 4.0, 9.0, 12.0 ] );\n > var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > base.strided.ssqrt.ndarray( x.length, x, 1, 0, y, 1, 0 )\n [ 0.0, 2.0, 3.0, ~3.464 ]\n\n // Advanced indexing:\n > x = new Float32Array( [ 0.0, 4.0, 9.0, 12.0 ] );\n > y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > var N = base.floor( x.length / 2 );\n > base.strided.ssqrt.ndarray( N, x, 2, 1, y, -1, y.length-1 )\n [ 0.0, 0.0, ~3.464, 2.0 ]\n\n See Also\n --------\n base.strided.dsqrt, base.strided.scbrt, strided.sqrt, base.strided.srsqrt\n"
base.strided.ssqrt.ndarray,"\nbase.strided.ssqrt.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )\n Computes the principal square root for each element in a single-precision\n floating-point strided array `x` and assigns the results to elements in a\n single-precision floating-point strided array `y` using alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offsetX` and `offsetY` parameters support indexing semantics\n based on starting indices.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Index increment for `x`.\n\n offsetX: integer\n Starting index for `x`.\n\n y: Float32Array\n Destination array.\n\n strideY: integer\n Index increment for `y`.\n\n offsetY: integer\n Starting index for `y`.\n\n Returns\n -------\n y: Float32Array\n Input array `y`.\n\n Examples\n --------\n // Standard usage:\n > var x = new Float32Array( [ 0.0, 4.0, 9.0, 12.0 ] );\n > var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > base.strided.ssqrt.ndarray( x.length, x, 1, 0, y, 1, 0 )\n [ 0.0, 2.0, 3.0, ~3.464 ]\n\n // Advanced indexing:\n > x = new Float32Array( [ 0.0, 4.0, 9.0, 12.0 ] );\n > y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > var N = base.floor( x.length / 2 );\n > base.strided.ssqrt.ndarray( N, x, 2, 1, y, -1, y.length-1 )\n [ 0.0, 0.0, ~3.464, 2.0 ]\n\n See Also\n --------\n base.strided.dsqrt, base.strided.scbrt, strided.sqrt, base.strided.srsqrt"
-base.strided.sstdev,"\nbase.strided.sstdev( N, correction, x, strideX )\n Computes the standard deviation of a single-precision floating-point strided\n array.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdev( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.sstdev( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.sstdev( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.sstdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var N = base.floor( x.length / 2 );\n > base.strided.sstdev.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdev, base.strided.snanstdev, base.strided.stdev, base.strided.svariance\n"
-base.strided.sstdev.ndarray,"\nbase.strided.sstdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var N = base.floor( x.length / 2 );\n > base.strided.sstdev.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdev, base.strided.snanstdev, base.strided.stdev, base.strided.svariance"
-base.strided.sstdevch,"\nbase.strided.sstdevch( N, correction, x, strideX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass trial mean algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevch( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.sstdevch( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.sstdevch( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.sstdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass trial mean algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevch.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevch, base.strided.snanstdevch, base.strided.sstdev, base.strided.stdevch, base.strided.svariancech\n"
-base.strided.sstdevch.ndarray,"\nbase.strided.sstdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass trial mean algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevch.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevch, base.strided.snanstdevch, base.strided.sstdev, base.strided.stdevch, base.strided.svariancech"
-base.strided.sstdevpn,"\nbase.strided.sstdevpn( N, correction, x, strideX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a two-pass algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevpn( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.sstdevpn( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.sstdevpn( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.sstdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a two-pass algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevpn.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevpn, base.strided.snanstdevpn, base.strided.sstdev, base.strided.stdevpn, base.strided.svariancepn\n"
-base.strided.sstdevpn.ndarray,"\nbase.strided.sstdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a two-pass algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevpn.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevpn, base.strided.snanstdevpn, base.strided.sstdev, base.strided.stdevpn, base.strided.svariancepn"
-base.strided.sstdevtk,"\nbase.strided.sstdevtk( N, correction, x, strideX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass textbook algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevtk( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.sstdevtk( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.sstdevtk( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.sstdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass textbook algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevtk.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevtk, base.strided.snanstdevtk, base.strided.sstdev, base.strided.stdevtk, base.strided.svariancetk\n"
-base.strided.sstdevtk.ndarray,"\nbase.strided.sstdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass textbook algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevtk.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevtk, base.strided.snanstdevtk, base.strided.sstdev, base.strided.stdevtk, base.strided.svariancetk"
-base.strided.sstdevwd,"\nbase.strided.sstdevwd( N, correction, x, stride )\n Computes the standard deviation of a single-precision floating-point strided\n array using Welford's algorithm.\n\n The `N` and `stride` parameters determine which elements in `x` are accessed\n at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n stride: integer\n Index increment.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevwd( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and `stride` parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > var N = base.floor( x.length / 2 );\n > var stride = 2;\n > base.strided.sstdevwd( N, 1, x, stride )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > N = base.floor( x0.length / 2 );\n > stride = 2;\n > base.strided.sstdevwd( N, 1, x1, stride )\n ~2.0817\n\nbase.strided.sstdevwd.ndarray( N, correction, x, stride, offset )\n Computes the standard deviation of a single-precision floating-point strided\n array using Welford's algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n stride: integer\n Index increment.\n\n offset: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var N = base.floor( x.length / 2 );\n > base.strided.sstdevwd.ndarray( N, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevwd, base.strided.snanstdevwd, base.strided.sstdev, base.strided.stdevwd, base.strided.svariancewd\n"
-base.strided.sstdevwd.ndarray,"\nbase.strided.sstdevwd.ndarray( N, correction, x, stride, offset )\n Computes the standard deviation of a single-precision floating-point strided\n array using Welford's algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n stride: integer\n Index increment.\n\n offset: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var N = base.floor( x.length / 2 );\n > base.strided.sstdevwd.ndarray( N, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevwd, base.strided.snanstdevwd, base.strided.sstdev, base.strided.stdevwd, base.strided.svariancewd"
-base.strided.sstdevyc,"\nbase.strided.sstdevyc( N, correction, x, strideX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass algorithm proposed by Youngs and Cramer.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevyc( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.sstdevyc( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.sstdevyc( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.sstdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass algorithm proposed by Youngs and Cramer and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevyc.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevyc.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevyc, base.strided.snanstdevyc, base.strided.sstdev, base.strided.stdevyc, base.strided.svarianceyc\n"
-base.strided.sstdevyc.ndarray,"\nbase.strided.sstdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass algorithm proposed by Youngs and Cramer and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevyc.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevyc.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevyc, base.strided.snanstdevyc, base.strided.sstdev, base.strided.stdevyc, base.strided.svarianceyc"
+base.strided.sstdev,"\nbase.strided.sstdev( N, correction, x, strideX )\n Computes the standard deviation of a single-precision floating-point strided\n array.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdev( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.sstdev( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.sstdev( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.sstdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var N = base.floor( x.length / 2 );\n > base.strided.sstdev.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdev, base.strided.snanstdev, base.strided.svariance\n"
+base.strided.sstdev.ndarray,"\nbase.strided.sstdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var N = base.floor( x.length / 2 );\n > base.strided.sstdev.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdev, base.strided.snanstdev, base.strided.svariance"
+base.strided.sstdevch,"\nbase.strided.sstdevch( N, correction, x, strideX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass trial mean algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevch( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.sstdevch( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.sstdevch( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.sstdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass trial mean algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevch.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevch, base.strided.snanstdevch, base.strided.sstdev, base.strided.svariancech\n"
+base.strided.sstdevch.ndarray,"\nbase.strided.sstdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass trial mean algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevch.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevch, base.strided.snanstdevch, base.strided.sstdev, base.strided.svariancech"
+base.strided.sstdevpn,"\nbase.strided.sstdevpn( N, correction, x, strideX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a two-pass algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevpn( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.sstdevpn( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.sstdevpn( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.sstdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a two-pass algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevpn.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevpn, base.strided.snanstdevpn, base.strided.sstdev, base.strided.svariancepn\n"
+base.strided.sstdevpn.ndarray,"\nbase.strided.sstdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a two-pass algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevpn.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevpn, base.strided.snanstdevpn, base.strided.sstdev, base.strided.svariancepn"
+base.strided.sstdevtk,"\nbase.strided.sstdevtk( N, correction, x, strideX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass textbook algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevtk( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.sstdevtk( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.sstdevtk( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.sstdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass textbook algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevtk.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevtk, base.strided.snanstdevtk, base.strided.sstdev, base.strided.svariancetk\n"
+base.strided.sstdevtk.ndarray,"\nbase.strided.sstdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass textbook algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevtk.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevtk, base.strided.snanstdevtk, base.strided.sstdev, base.strided.svariancetk"
+base.strided.sstdevwd,"\nbase.strided.sstdevwd( N, correction, x, stride )\n Computes the standard deviation of a single-precision floating-point strided\n array using Welford's algorithm.\n\n The `N` and `stride` parameters determine which elements in `x` are accessed\n at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n stride: integer\n Index increment.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevwd( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and `stride` parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > var N = base.floor( x.length / 2 );\n > var stride = 2;\n > base.strided.sstdevwd( N, 1, x, stride )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > N = base.floor( x0.length / 2 );\n > stride = 2;\n > base.strided.sstdevwd( N, 1, x1, stride )\n ~2.0817\n\nbase.strided.sstdevwd.ndarray( N, correction, x, stride, offset )\n Computes the standard deviation of a single-precision floating-point strided\n array using Welford's algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n stride: integer\n Index increment.\n\n offset: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var N = base.floor( x.length / 2 );\n > base.strided.sstdevwd.ndarray( N, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevwd, base.strided.snanstdevwd, base.strided.sstdev, base.strided.svariancewd\n"
+base.strided.sstdevwd.ndarray,"\nbase.strided.sstdevwd.ndarray( N, correction, x, stride, offset )\n Computes the standard deviation of a single-precision floating-point strided\n array using Welford's algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n stride: integer\n Index increment.\n\n offset: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var N = base.floor( x.length / 2 );\n > base.strided.sstdevwd.ndarray( N, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevwd, base.strided.snanstdevwd, base.strided.sstdev, base.strided.svariancewd"
+base.strided.sstdevyc,"\nbase.strided.sstdevyc( N, correction, x, strideX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass algorithm proposed by Youngs and Cramer.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevyc( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.sstdevyc( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.sstdevyc( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.sstdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass algorithm proposed by Youngs and Cramer and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevyc.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevyc.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevyc, base.strided.snanstdevyc, base.strided.sstdev, base.strided.svarianceyc\n"
+base.strided.sstdevyc.ndarray,"\nbase.strided.sstdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a single-precision floating-point strided\n array using a one-pass algorithm proposed by Youngs and Cramer and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.sstdevyc.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.sstdevyc.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevyc, base.strided.snanstdevyc, base.strided.sstdev, base.strided.svarianceyc"
base.strided.ssum,"\nbase.strided.ssum( N, x, strideX )\n Computes the sum of single-precision floating-point strided array elements.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `0.0`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.ssum( x.length, x, 1 )\n 1.0\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.ssum( 3, x, 2 )\n 1.0\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.ssum( 3, x1, 2 )\n -1.0\n\n\nbase.strided.ssum.ndarray( N, x, strideX, offsetX )\n Computes the sum of single-precision floating-point strided array elements\n using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a starting\n index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.ssum.ndarray( x.length, x, 1, 0 )\n 1.0\n\n // Using offset parameter:\n > x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.ssum.ndarray( 3, x, 2, 1 )\n -1.0\n\n See Also\n --------\n base.strided.dsum, base.strided.smean, base.strided.snansum, base.strided.gsum\n"
base.strided.ssum.ndarray,"\nbase.strided.ssum.ndarray( N, x, strideX, offsetX )\n Computes the sum of single-precision floating-point strided array elements\n using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a starting\n index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.ssum.ndarray( x.length, x, 1, 0 )\n 1.0\n\n // Using offset parameter:\n > x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.ssum.ndarray( 3, x, 2, 1 )\n -1.0\n\n See Also\n --------\n base.strided.dsum, base.strided.smean, base.strided.snansum, base.strided.gsum"
base.strided.ssumkbn,"\nbase.strided.ssumkbn( N, x, strideX )\n Computes the sum of single-precision floating-point strided array elements\n using an improved Kahan–Babuška algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `0.0`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.ssumkbn( x.length, x, 1 )\n 1.0\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.ssumkbn( 3, x, 2 )\n 1.0\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.ssumkbn( 3, x1, 2 )\n -1.0\n\n\nbase.strided.ssumkbn.ndarray( N, x, strideX, offsetX )\n Computes the sum of single-precision floating-point strided array elements\n using an improved Kahan–Babuška algorithm and alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a starting\n index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.ssumkbn.ndarray( x.length, x, 1, 0 )\n 1.0\n\n // Using offset parameter:\n > x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.ssumkbn.ndarray( 3, x, 2, 1 )\n -1.0\n\n See Also\n --------\n base.strided.dsumkbn, base.strided.gsumkbn, base.strided.snansumkbn, base.strided.ssum, base.strided.ssumkbn2, base.strided.ssumors, base.strided.ssumpw\n"
@@ -2474,16 +2474,16 @@ base.strided.sswap,"\nbase.strided.sswap( N, x, strideX, y, strideY )\n Inter
base.strided.sswap.ndarray,"\nbase.strided.sswap.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )\n Interchanges two single-precision floating-point vectors using alternative\n indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameters support indexing semantics based on starting\n indices.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n First input array.\n\n strideX: integer\n Index increment for `x`.\n\n offsetX: integer\n Starting index for `x`.\n\n y: Float32Array\n Second input array.\n\n strideY: integer\n Index increment for `y`.\n\n offsetY: integer\n Starting index for `y`.\n\n Returns\n -------\n y: Float32Array\n Second input array.\n\n Examples\n --------\n // Standard usage:\n > var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );\n > var y = new Float32Array( [ 6.0, 7.0, 8.0, 9.0, 10.0 ] );\n > base.strided.sswap.ndarray( x.length, x, 1, 0, y, 1, 0 )\n [ 1.0, 2.0, 3.0, 4.0, 5.0 ]\n\n // Advanced indexing:\n > x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );\n > y = new Float32Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );\n > base.strided.sswap.ndarray( 3, x, 2, 1, y, -1, y.length-1 )\n [ 7.0, 8.0, 9.0, 6.0, 4.0, 2.0 ]\n\n See Also\n --------\n base.strided.dswap, base.strided.gswap, base.strided.scopy, sswap"
base.strided.stdev,"\nbase.strided.stdev( N, correction, x, strideX )\n Computes the standard deviation of a strided array.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdev( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.stdev( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.stdev( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.stdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using alternative\n indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdev.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdev, base.strided.nanstdev, base.strided.sstdev, base.strided.variance\n"
base.strided.stdev.ndarray,"\nbase.strided.stdev.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using alternative\n indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdev.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdev.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdev, base.strided.nanstdev, base.strided.sstdev, base.strided.variance"
-base.strided.stdevch,"\nbase.strided.stdevch( N, correction, x, strideX )\n Computes the standard deviation of a strided array using a one-pass trial\n mean algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevch( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.stdevch( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.stdevch( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.stdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a one-pass trial\n mean algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevch.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevch, base.strided.nanstdevch, base.strided.sstdevch, base.strided.stdev, base.strided.variancech\n"
-base.strided.stdevch.ndarray,"\nbase.strided.stdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a one-pass trial\n mean algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevch.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevch, base.strided.nanstdevch, base.strided.sstdevch, base.strided.stdev, base.strided.variancech"
-base.strided.stdevpn,"\nbase.strided.stdevpn( N, correction, x, strideX )\n Computes the standard deviation of a strided array using a two-pass\n algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevpn( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.stdevpn( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.stdevpn( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.stdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a two-pass\n algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevpn.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevpn, base.strided.nanstdevpn, base.strided.sstdevpn, base.strided.stdev, base.strided.variancepn\n"
-base.strided.stdevpn.ndarray,"\nbase.strided.stdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a two-pass\n algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevpn.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevpn, base.strided.nanstdevpn, base.strided.sstdevpn, base.strided.stdev, base.strided.variancepn"
-base.strided.stdevtk,"\nbase.strided.stdevtk( N, correction, x, strideX )\n Computes the standard deviation of a strided array using a one-pass textbook\n algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevtk( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.stdevtk( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.stdevtk( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.stdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a one-pass textbook\n algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevtk.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevtk, base.strided.nanstdevtk, base.strided.sstdevtk, base.strided.stdev, base.strided.variancetk\n"
-base.strided.stdevtk.ndarray,"\nbase.strided.stdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a one-pass textbook\n algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevtk.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevtk, base.strided.nanstdevtk, base.strided.sstdevtk, base.strided.stdev, base.strided.variancetk"
-base.strided.stdevwd,"\nbase.strided.stdevwd( N, correction, x, strideX )\n Computes the standard deviation of a strided array using Welford's\n algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevwd( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.stdevwd( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.stdevwd( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.stdevwd.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using Welford's algorithm\n and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevwd.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevwd, base.strided.nanstdevwd, base.strided.sstdevwd, base.strided.stdev, base.strided.variancewd\n"
-base.strided.stdevwd.ndarray,"\nbase.strided.stdevwd.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using Welford's algorithm\n and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevwd.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevwd, base.strided.nanstdevwd, base.strided.sstdevwd, base.strided.stdev, base.strided.variancewd"
-base.strided.stdevyc,"\nbase.strided.stdevyc( N, correction, x, strideX )\n Computes the standard deviation of a strided array using a one-pass\n algorithm proposed by Youngs and Cramer.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevyc( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.stdevyc( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.stdevyc( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.stdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a one-pass\n algorithm proposed by Youngs and Cramer and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevyc.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevyc.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevyc, base.strided.nanstdevyc, base.strided.sstdevyc, base.strided.stdev, base.strided.varianceyc\n"
-base.strided.stdevyc.ndarray,"\nbase.strided.stdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a one-pass\n algorithm proposed by Youngs and Cramer and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevyc.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevyc.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevyc, base.strided.nanstdevyc, base.strided.sstdevyc, base.strided.stdev, base.strided.varianceyc"
+base.strided.stdevch,"\nbase.strided.stdevch( N, correction, x, strideX )\n Computes the standard deviation of a strided array using a one-pass trial\n mean algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevch( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.stdevch( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.stdevch( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.stdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a one-pass trial\n mean algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevch.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevch, base.strided.nanstdevch, base.strided.sstdevch, base.strided.variancech\n"
+base.strided.stdevch.ndarray,"\nbase.strided.stdevch.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a one-pass trial\n mean algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevch.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevch.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevch, base.strided.nanstdevch, base.strided.sstdevch, base.strided.variancech"
+base.strided.stdevpn,"\nbase.strided.stdevpn( N, correction, x, strideX )\n Computes the standard deviation of a strided array using a two-pass\n algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevpn( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.stdevpn( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.stdevpn( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.stdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a two-pass\n algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevpn.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevpn, base.strided.nanstdevpn, base.strided.sstdevpn, base.strided.variancepn\n"
+base.strided.stdevpn.ndarray,"\nbase.strided.stdevpn.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a two-pass\n algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevpn.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevpn.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevpn, base.strided.nanstdevpn, base.strided.sstdevpn, base.strided.variancepn"
+base.strided.stdevtk,"\nbase.strided.stdevtk( N, correction, x, strideX )\n Computes the standard deviation of a strided array using a one-pass textbook\n algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevtk( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.stdevtk( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.stdevtk( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.stdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a one-pass textbook\n algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevtk.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevtk, base.strided.nanstdevtk, base.strided.sstdevtk, base.strided.variancetk\n"
+base.strided.stdevtk.ndarray,"\nbase.strided.stdevtk.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a one-pass textbook\n algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevtk.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevtk.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevtk, base.strided.nanstdevtk, base.strided.sstdevtk, base.strided.variancetk"
+base.strided.stdevwd,"\nbase.strided.stdevwd( N, correction, x, strideX )\n Computes the standard deviation of a strided array using Welford's\n algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevwd( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.stdevwd( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.stdevwd( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.stdevwd.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using Welford's algorithm\n and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevwd.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevwd, base.strided.nanstdevwd, base.strided.sstdevwd, base.strided.variancewd\n"
+base.strided.stdevwd.ndarray,"\nbase.strided.stdevwd.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using Welford's algorithm\n and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevwd.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevwd.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevwd, base.strided.nanstdevwd, base.strided.sstdevwd, base.strided.variancewd"
+base.strided.stdevyc,"\nbase.strided.stdevyc( N, correction, x, strideX )\n Computes the standard deviation of a strided array using a one-pass\n algorithm proposed by Youngs and Cramer.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevyc( x.length, 1, x, 1 )\n ~2.0817\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.stdevyc( 3, 1, x, 2 )\n ~2.0817\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.stdevyc( 3, 1, x1, 2 )\n ~2.0817\n\n\nbase.strided.stdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a one-pass\n algorithm proposed by Youngs and Cramer and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevyc.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevyc.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevyc, base.strided.nanstdevyc, base.strided.sstdevyc, base.strided.varianceyc\n"
+base.strided.stdevyc.ndarray,"\nbase.strided.stdevyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the standard deviation of a strided array using a one-pass\n algorithm proposed by Youngs and Cramer and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the standard deviation according to `N - c` where `c` corresponds to\n the provided degrees of freedom adjustment. When computing the standard\n deviation of a population, setting this parameter to `0` is the standard\n choice (i.e., the provided array contains data constituting an entire\n population). When computing the corrected sample standard deviation,\n setting this parameter to `1` is the standard choice (i.e., the provided\n array contains data sampled from a larger population; this is commonly\n referred to as Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The standard deviation.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.stdevyc.ndarray( x.length, 1, x, 1, 0 )\n ~2.0817\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.stdevyc.ndarray( 3, 1, x, 2, 1 )\n ~2.0817\n\n See Also\n --------\n base.strided.dstdevyc, base.strided.nanstdevyc, base.strided.sstdevyc, base.strided.varianceyc"
base.strided.strunc,"\nbase.strided.strunc( N, x, strideX, y, strideY )\n Rounds each element in a single-precision floating-point strided array `x`\n toward zero and assigns the results to elements in a single-precision\n floating-point strided array `y`.\n\n The `N` and `stride` parameters determine which elements in `x` and `y` are\n accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use typed\n array views.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Index increment for `x`.\n\n y: Float32Array\n Destination array.\n\n strideY: integer\n Index increment for `y`.\n\n Returns\n -------\n y: Float32Array\n Input array `y`.\n\n Examples\n --------\n // Standard usage:\n > var x = new Float32Array( [ 1.1, 2.5, -3.5, 4.0 ] );\n > var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > base.strided.strunc( x.length, x, 1, y, 1 )\n [ 1.0, 2.0, -3.0, 4.0 ]\n\n // Using `N` and `stride` parameters:\n > var N = base.floor( x.length / 2 );\n > y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > base.strided.strunc( N, x, 2, y, -1 )\n [ -3.0, 1.0, 0.0, 0.0 ]\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.1, 2.5, -3.5, 4.0 ] );\n > var y0 = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > var y1 = new Float32Array( y0.buffer, y0.BYTES_PER_ELEMENT*2 );\n > N = base.floor( x0.length / 2 );\n > base.strided.strunc( N, x1, -2, y1, 1 )\n [ 4.0, 2.0 ]\n > y0\n [ 0.0, 0.0, 4.0, 2.0 ]\n\n\nbase.strided.strunc.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )\n Rounds each element in a single-precision floating-point strided array `x`\n toward zero and assigns the results to elements in a single-precision\n floating-point strided array `y` using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offsetX` and `offsetY` parameters support indexing semantics\n based on starting indices.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Index increment for `x`.\n\n offsetX: integer\n Starting index for `x`.\n\n y: Float32Array\n Destination array.\n\n strideY: integer\n Index increment for `y`.\n\n offsetY: integer\n Starting index for `y`.\n\n Returns\n -------\n y: Float32Array\n Input array `y`.\n\n Examples\n --------\n // Standard usage:\n > var x = new Float32Array( [ 1.1, 2.5, -3.5, 4.0 ] );\n > var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > base.strided.strunc.ndarray( x.length, x, 1, 0, y, 1, 0 )\n [ 1.0, 2.0, -3.0, 4.0 ]\n\n // Advanced indexing:\n > x = new Float32Array( [ 1.1, 2.5, -3.5, 4.0 ] );\n > y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > var N = base.floor( x.length / 2 );\n > base.strided.strunc.ndarray( N, x, 2, 1, y, -1, y.length-1 )\n [ 0.0, 0.0, 4.0, 2.0 ]\n\n See Also\n --------\n base.strided.dtrunc, base.strided.sceil, base.strided.sfloor, strided.trunc\n"
base.strided.strunc.ndarray,"\nbase.strided.strunc.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )\n Rounds each element in a single-precision floating-point strided array `x`\n toward zero and assigns the results to elements in a single-precision\n floating-point strided array `y` using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offsetX` and `offsetY` parameters support indexing semantics\n based on starting indices.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Index increment for `x`.\n\n offsetX: integer\n Starting index for `x`.\n\n y: Float32Array\n Destination array.\n\n strideY: integer\n Index increment for `y`.\n\n offsetY: integer\n Starting index for `y`.\n\n Returns\n -------\n y: Float32Array\n Input array `y`.\n\n Examples\n --------\n // Standard usage:\n > var x = new Float32Array( [ 1.1, 2.5, -3.5, 4.0 ] );\n > var y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > base.strided.strunc.ndarray( x.length, x, 1, 0, y, 1, 0 )\n [ 1.0, 2.0, -3.0, 4.0 ]\n\n // Advanced indexing:\n > x = new Float32Array( [ 1.1, 2.5, -3.5, 4.0 ] );\n > y = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > var N = base.floor( x.length / 2 );\n > base.strided.strunc.ndarray( N, x, 2, 1, y, -1, y.length-1 )\n [ 0.0, 0.0, 4.0, 2.0 ]\n\n See Also\n --------\n base.strided.dtrunc, base.strided.sceil, base.strided.sfloor, strided.trunc"
base.strided.svariance,"\nbase.strided.svariance( N, correction, x, strideX )\n Computes the variance of a single-precision floating-point strided array.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride Length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.svariance( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\n > base.strided.svariance( 3, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.svariance( 3, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.svariance.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a single-precision floating-point strided array\n using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Float32Array\n Input array.\n\n strideX: integer\n Stride Length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n > base.strided.svariance.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > var x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > base.strided.svariance.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariance, base.strided.snanvariance, base.strided.sstdev, base.strided.variance\n"
@@ -2506,18 +2506,18 @@ base.strided.unaryBy,"\nbase.strided.unaryBy( arrays, shape, strides, fcn, clbk[
base.strided.unaryBy.ndarray,"\nbase.strided.unaryBy.ndarray( arrays, shape, strides, offsets, fcn, clbk[, thisArg] )\n Applies a unary function to each element retrieved from a strided input\n array according to a callback function and assigns results to a strided\n output array using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offsest parameters support indexing semantics\n based on starting indices.\n\n Parameters\n ----------\n arrays: ArrayLikeObject\n Array-like object containing one strided input array and one strided\n output array.\n\n shape: ArrayLikeObject\n Array-like object containing a single element, the number of indexed\n elements.\n\n strides: ArrayLikeObject\n Array-like object containing the stride lengths for the strided input\n and output arrays.\n\n offsets: ArrayLikeObject\n Array-like object containing the starting indices (i.e., index offsets)\n for the strided input and output arrays.\n\n fcn: Function\n Unary function to apply to callback return values.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Examples\n --------\n // Standard usage:\n > var x = [ 1.0, -2.0, 3.0, -4.0 ];\n > var y = [ 0.0, 0.0, 0.0, 0.0 ];\n > var sh = [ x.length ];\n > var st = [ 1, 1 ];\n > var o = [ 0, 0 ];\n > function clbk( v ) { return v * 2.0; };\n > base.strided.unaryBy.ndarray( [ x, y ], sh, st, o, base.abs, clbk );\n > y\n [ 2.0, 4.0, 6.0, 8.0 ]\n\n // Advanced indexing:\n > x = [ 1.0, -2.0, 3.0, -4.0 ];\n > y = [ 0.0, 0.0, 0.0, 0.0 ];\n > sh = [ 2 ];\n > st = [ 2, -1 ];\n > o = [ 1, y.length-1 ];\n > base.strided.unaryBy.ndarray( [ x, y ], sh, st, o, base.abs, clbk );\n > y\n [ 0.0, 0.0, 8.0, 4.0 ]\n\n See Also\n --------\n base.strided.mapBy, base.strided.unary"
base.strided.unaryDtypeSignatures,"\nbase.strided.unaryDtypeSignatures( dtypes1, dtypes2[, options] )\n Generates a list of unary interface signatures from strided array data\n types.\n\n The function returns a strided array having a stride length of two (i.e.,\n every two elements define a unary interface signature).\n\n For each signature (i.e., set of two consecutive non-overlapping strided\n array elements), the first element is the input data type and the second\n element is the return data type.\n\n All signatures follow type promotion rules.\n\n Parameters\n ----------\n dtypes1: ArrayLike\n List of supported data types for the input argument.\n\n dtypes2: ArrayLike\n List of supported data types for the output argument.\n\n options: Object (optional)\n Options.\n\n options.enums: boolean (optional)\n Boolean flag indicating whether to return signatures as a list of data\n type enumeration constants. Default: false.\n\n Returns\n -------\n out: Array\n Strided array containing unary interface signatures.\n\n Examples\n --------\n > var dt = strided.dataTypes();\n > var out = base.strided.unaryDtypeSignatures( dt, dt )\n [...]\n\n See Also\n --------\n base.strided.unarySignatureCallbacks\n"
base.strided.unarySignatureCallbacks,"\nbase.strided.unarySignatureCallbacks( table, signatures )\n Assigns callbacks to unary interfaces according to type promotion rules.\n\n Parameters\n ----------\n table: Object\n Object mapping callbacks to data types.\n\n table.default: Function\n Default callback.\n\n table.complex64: Function\n Callback to be invoked when input arrays promote to single-precision\n complex floating-point numbers.\n\n table.complex128: Function\n Callback to be invoked when input arrays promote to double precision\n complex floating-point numbers.\n\n signatures: ArrayLike\n Strided array containing unary interface signatures.\n\n Returns\n -------\n out: Array\n List of callbacks, one per signature.\n\n Examples\n --------\n > var dt = strided.dataTypes();\n > var sigs = base.strided.unaryDtypeSignatures( dt, dt );\n > var t = {\n ... 'default': base.identity,\n ... 'complex64': base.cidentityf,\n ... 'complex128': base.cidentity\n ... };\n > var out = base.strided.unarySignatureCallbacks( t, sigs )\n [...]\n\n See Also\n --------\n base.strided.unaryDtypeSignatures\n"
-base.strided.variance,"\nbase.strided.variance( N, correction, x, strideX )\n Computes the variance of a strided array.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variance( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.variance( 3, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.variance( 3, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.variance.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variance.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variance.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariance, base.strided.nanvariance, base.strided.stdev, base.strided.svariance\n"
-base.strided.variance.ndarray,"\nbase.strided.variance.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variance.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variance.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariance, base.strided.nanvariance, base.strided.stdev, base.strided.svariance"
-base.strided.variancech,"\nbase.strided.variancech( N, correction, x, strideX )\n Computes the variance of a strided array using a one-pass trial mean\n algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancech( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.variancech( 3, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.variancech( 3, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.variancech.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a one-pass trial mean\n algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancech.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancech.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancech, base.strided.nanvariancech, base.strided.stdevch, base.strided.variance\n"
-base.strided.variancech.ndarray,"\nbase.strided.variancech.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a one-pass trial mean\n algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancech.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancech.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancech, base.strided.nanvariancech, base.strided.stdevch, base.strided.variance"
-base.strided.variancepn,"\nbase.strided.variancepn( N, correction, x, strideX )\n Computes the variance of a strided array using a two-pass algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancepn( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.variancepn( 3, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.variancepn( 3, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.variancepn.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a two-pass algorithm and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancepn.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancepn.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancepn, base.strided.nanvariancepn, base.strided.stdevpn, base.strided.variance\n"
-base.strided.variancepn.ndarray,"\nbase.strided.variancepn.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a two-pass algorithm and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancepn.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancepn.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancepn, base.strided.nanvariancepn, base.strided.stdevpn, base.strided.variance"
-base.strided.variancetk,"\nbase.strided.variancetk( N, correction, x, strideX )\n Computes the variance of a strided array using a one-pass textbook\n algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancetk( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.variancetk( 3, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.variancetk( 3, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.variancetk.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a one-pass textbook algorithm\n and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancetk.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancetk.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancetk, base.strided.nanvariancetk, base.strided.stdevtk, base.strided.variance\n"
-base.strided.variancetk.ndarray,"\nbase.strided.variancetk.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a one-pass textbook algorithm\n and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancetk.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancetk.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancetk, base.strided.nanvariancetk, base.strided.stdevtk, base.strided.variance"
-base.strided.variancewd,"\nbase.strided.variancewd( N, correction, x, strideX )\n Computes the variance of a strided array using Welford's algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancewd( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.variancewd( 3, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.variancewd( 3, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.variancewd.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using Welford's algorithm and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancewd.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancewd.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancewd, base.strided.nanvariancewd, base.strided.stdevwd, base.strided.variance\n"
-base.strided.variancewd.ndarray,"\nbase.strided.variancewd.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using Welford's algorithm and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancewd.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancewd.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancewd, base.strided.nanvariancewd, base.strided.stdevwd, base.strided.variance"
-base.strided.varianceyc,"\nbase.strided.varianceyc( N, correction, x, strideX )\n Computes the variance of a strided array using a one-pass algorithm proposed\n by Youngs and Cramer.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length for `x`.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.varianceyc( x.length, 1.0, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.varianceyc( 3, 1.0, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.varianceyc( 3, 1.0, x1, 2 )\n ~4.3333\n\n\nbase.strided.varianceyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a one-pass algorithm proposed\n by Youngs and Cramer and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length for `x`.\n\n offsetX: integer\n Starting index for `x`.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.varianceyc.ndarray( x.length, 1.0, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.varianceyc.ndarray( 3, 1.0, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvarianceyc, base.strided.nanvarianceyc, base.strided.stdevyc, base.strided.variance\n"
-base.strided.varianceyc.ndarray,"\nbase.strided.varianceyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a one-pass algorithm proposed\n by Youngs and Cramer and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length for `x`.\n\n offsetX: integer\n Starting index for `x`.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.varianceyc.ndarray( x.length, 1.0, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.varianceyc.ndarray( 3, 1.0, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvarianceyc, base.strided.nanvarianceyc, base.strided.stdevyc, base.strided.variance"
+base.strided.variance,"\nbase.strided.variance( N, correction, x, strideX )\n Computes the variance of a strided array.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variance( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.variance( 3, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.variance( 3, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.variance.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variance.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variance.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariance, base.strided.nanvariance, base.strided.svariance\n"
+base.strided.variance.ndarray,"\nbase.strided.variance.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using alternative indexing\n semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variance.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variance.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariance, base.strided.nanvariance, base.strided.svariance"
+base.strided.variancech,"\nbase.strided.variancech( N, correction, x, strideX )\n Computes the variance of a strided array using a one-pass trial mean\n algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancech( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.variancech( 3, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.variancech( 3, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.variancech.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a one-pass trial mean\n algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancech.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancech.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancech, base.strided.nanvariancech, base.strided.variance\n"
+base.strided.variancech.ndarray,"\nbase.strided.variancech.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a one-pass trial mean\n algorithm and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancech.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancech.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancech, base.strided.nanvariancech, base.strided.variance"
+base.strided.variancepn,"\nbase.strided.variancepn( N, correction, x, strideX )\n Computes the variance of a strided array using a two-pass algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancepn( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.variancepn( 3, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.variancepn( 3, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.variancepn.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a two-pass algorithm and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancepn.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancepn.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancepn, base.strided.nanvariancepn, base.strided.variance\n"
+base.strided.variancepn.ndarray,"\nbase.strided.variancepn.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a two-pass algorithm and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancepn.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancepn.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancepn, base.strided.nanvariancepn, base.strided.variance"
+base.strided.variancetk,"\nbase.strided.variancetk( N, correction, x, strideX )\n Computes the variance of a strided array using a one-pass textbook\n algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancetk( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.variancetk( 3, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.variancetk( 3, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.variancetk.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a one-pass textbook algorithm\n and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancetk.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancetk.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancetk, base.strided.nanvariancetk, base.strided.variance\n"
+base.strided.variancetk.ndarray,"\nbase.strided.variancetk.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a one-pass textbook algorithm\n and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancetk.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancetk.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancetk, base.strided.nanvariancetk, base.strided.variance"
+base.strided.variancewd,"\nbase.strided.variancewd( N, correction, x, strideX )\n Computes the variance of a strided array using Welford's algorithm.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancewd( x.length, 1, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.variancewd( 3, 1, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.variancewd( 3, 1, x1, 2 )\n ~4.3333\n\n\nbase.strided.variancewd.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using Welford's algorithm and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancewd.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancewd.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancewd, base.strided.nanvariancewd, base.strided.variance\n"
+base.strided.variancewd.ndarray,"\nbase.strided.variancewd.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using Welford's algorithm and\n alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the `offset` parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length.\n\n offsetX: integer\n Starting index.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.variancewd.ndarray( x.length, 1, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.variancewd.ndarray( 3, 1, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvariancewd, base.strided.nanvariancewd, base.strided.variance"
+base.strided.varianceyc,"\nbase.strided.varianceyc( N, correction, x, strideX )\n Computes the variance of a strided array using a one-pass algorithm proposed\n by Youngs and Cramer.\n\n The `N` and stride parameters determine which elements in the strided array\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use a typed\n array view.\n\n If `N <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length for `x`.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.varianceyc( x.length, 1.0, x, 1 )\n ~4.3333\n\n // Using `N` and stride parameters:\n > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];\n > base.strided.varianceyc( 3, 1.0, x, 2 )\n ~4.3333\n\n // Using view offsets:\n > var x0 = new Float64Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\n > var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > base.strided.varianceyc( 3, 1.0, x1, 2 )\n ~4.3333\n\n\nbase.strided.varianceyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a one-pass algorithm proposed\n by Youngs and Cramer and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length for `x`.\n\n offsetX: integer\n Starting index for `x`.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.varianceyc.ndarray( x.length, 1.0, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.varianceyc.ndarray( 3, 1.0, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvarianceyc, base.strided.nanvarianceyc, base.strided.variance\n"
+base.strided.varianceyc.ndarray,"\nbase.strided.varianceyc.ndarray( N, correction, x, strideX, offsetX )\n Computes the variance of a strided array using a one-pass algorithm proposed\n by Youngs and Cramer and alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameter supports indexing semantics based on a\n starting index.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n correction: number\n Degrees of freedom adjustment. Setting this parameter to a value other\n than `0` has the effect of adjusting the divisor during the calculation\n of the variance according to `N - c` where `c` corresponds to the\n provided degrees of freedom adjustment. When computing the variance of a\n population, setting this parameter to `0` is the standard choice (i.e.,\n the provided array contains data constituting an entire population).\n When computing the unbiased sample variance, setting this parameter to\n `1` is the standard choice (i.e., the provided array contains data\n sampled from a larger population; this is commonly referred to as\n Bessel's correction).\n\n x: Array|TypedArray\n Input array.\n\n strideX: integer\n Stride length for `x`.\n\n offsetX: integer\n Starting index for `x`.\n\n Returns\n -------\n out: number\n The variance.\n\n Examples\n --------\n // Standard Usage:\n > var x = [ 1.0, -2.0, 2.0 ];\n > base.strided.varianceyc.ndarray( x.length, 1.0, x, 1, 0 )\n ~4.3333\n\n // Using offset parameter:\n > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];\n > base.strided.varianceyc.ndarray( 3, 1.0, x, 2, 1 )\n ~4.3333\n\n See Also\n --------\n base.strided.dvarianceyc, base.strided.nanvarianceyc, base.strided.variance"
base.strided.zmap,"\nbase.strided.zmap( N, x, strideX, y, strideY, fcn )\n Applies a unary function to a double-precision complex floating-point\n strided input array and assigns results to a double-precision complex\n floating-point strided output array.\n\n The `N` and stride parameters determine which elements in the strided arrays\n are accessed at runtime.\n\n Indexing is relative to the first index. To introduce an offset, use typed\n array views.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Complex128Array\n Input array.\n\n strideX: integer\n Index increment for `x`.\n\n y: Complex128Array\n Destination array.\n\n strideY: integer\n Index increment for `y`.\n\n fcn: Function\n Unary function to apply.\n\n Returns\n -------\n y: Complex128Array\n Input array `y`.\n\n Examples\n --------\n // Standard usage:\n > var xbuf = [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ];\n > var x = new Complex128Array( xbuf );\n > var y = new Complex128Array( x.length );\n > base.strided.zmap( x.length, x, 1, y, 1, base.cidentity );\n > var v = y.get( 0 )\n \n > var re = real( v )\n 1.0\n > var im = imag( v )\n 2.0\n\n // Using `N` and stride parameters:\n > y = new Complex128Array( x.length );\n > base.strided.zmap( 2, x, 2, y, -1, base.cidentity );\n > v = y.get( 0 )\n \n > re = real( v )\n 5.0\n > im = imag( v )\n 6.0\n\n // Using view offsets:\n > var x0 = new Complex128Array( xbuf );\n > var y0 = new Complex128Array( x0.length );\n > var x1 = new Complex128Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\n > var y1 = new Complex128Array( y0.buffer, y0.BYTES_PER_ELEMENT*2 );\n > base.strided.zmap( 2, x1, -2, y1, 1, base.cidentity );\n > v = y1.get( 0 )\n \n > re = real( v )\n 7.0\n > im = imag( v )\n 8.0\n\n\nbase.strided.zmap.ndarray( N, x, strideX, offsetX, y, strideY, offsetY, fcn )\n Applies a unary function to a double-precision complex floating-point\n strided input array and assigns results to a double-precision complex\n floating-point strided output array using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameters support indexing semantics based on starting\n indices.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Complex128Array\n Input array.\n\n strideX: integer\n Index increment for `x`.\n\n offsetX: integer\n Starting index for `x`.\n\n y: Complex128Array\n Destination array.\n\n strideY: integer\n Index increment for `y`.\n\n offsetY: integer\n Starting index for `y`.\n\n fcn: Function\n Unary function to apply.\n\n Returns\n -------\n y: Complex128Array\n Input array `y`.\n\n Examples\n --------\n // Standard usage:\n > var xbuf = [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ];\n > var x = new Complex128Array( xbuf );\n > var y = new Complex128Array( x.length );\n > base.strided.zmap.ndarray( x.length, x, 1, 0, y, 1, 0, base.cidentity );\n > var v = y.get( 0 )\n \n > var re = real( v )\n 1.0\n > var im = imag( v )\n 2.0\n\n // Advanced indexing:\n > x = new Complex128Array( xbuf );\n > y = new Complex128Array( x.length );\n > base.strided.zmap.ndarray( 2, x, 2, 1, y, -1, y.length-1, base.cidentity );\n > v = y.get( y.length-1 )\n \n > re = real( v )\n 3.0\n > im = imag( v )\n 4.0\n\n See Also\n --------\n base.strided.cmap, base.strided.unary\n"
base.strided.zmap.ndarray,"\nbase.strided.zmap.ndarray( N, x, strideX, offsetX, y, strideY, offsetY, fcn )\n Applies a unary function to a double-precision complex floating-point\n strided input array and assigns results to a double-precision complex\n floating-point strided output array using alternative indexing semantics.\n\n While typed array views mandate a view offset based on the underlying\n buffer, the offset parameters support indexing semantics based on starting\n indices.\n\n Parameters\n ----------\n N: integer\n Number of indexed elements.\n\n x: Complex128Array\n Input array.\n\n strideX: integer\n Index increment for `x`.\n\n offsetX: integer\n Starting index for `x`.\n\n y: Complex128Array\n Destination array.\n\n strideY: integer\n Index increment for `y`.\n\n offsetY: integer\n Starting index for `y`.\n\n fcn: Function\n Unary function to apply.\n\n Returns\n -------\n y: Complex128Array\n Input array `y`.\n\n Examples\n --------\n // Standard usage:\n > var xbuf = [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ];\n > var x = new Complex128Array( xbuf );\n > var y = new Complex128Array( x.length );\n > base.strided.zmap.ndarray( x.length, x, 1, 0, y, 1, 0, base.cidentity );\n > var v = y.get( 0 )\n \n > var re = real( v )\n 1.0\n > var im = imag( v )\n 2.0\n\n // Advanced indexing:\n > x = new Complex128Array( xbuf );\n > y = new Complex128Array( x.length );\n > base.strided.zmap.ndarray( 2, x, 2, 1, y, -1, y.length-1, base.cidentity );\n > v = y.get( y.length-1 )\n \n > re = real( v )\n 3.0\n > im = imag( v )\n 4.0\n\n See Also\n --------\n base.strided.cmap, base.strided.unary"
base.str2multislice,"\nbase.str2multislice( str )\n Parses a string-serialized MultiSlice object.\n\n The function returns `null` if provided an invalid string.\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n s: MultiSlice|null\n MultiSlice instance (or null).\n\n Examples\n --------\n > var s = new base.str2multislice( 'MultiSlice(null,null,null)' );\n > s.data\n [ null, null, null ]\n > s = new base.str2multislice( 'MultiSlice(10,Slice(0,10,1),null)' );\n > s.data\n [ 10, , null ]\n\n See Also\n --------\n base.str2slice\n"
diff --git a/lib/node_modules/@stdlib/repl/help/data/data.json b/lib/node_modules/@stdlib/repl/help/data/data.json
index ca122f8f5cd6..9101cabdc77d 100644
--- a/lib/node_modules/@stdlib/repl/help/data/data.json
+++ b/lib/node_modules/@stdlib/repl/help/data/data.json
@@ -1 +1 @@
-{"abs":"\nabs( x[, options] )\n Computes the absolute value.\n\n If provided a number, the function returns a number.\n\n If provided an ndarray or array-like object, the function performs element-\n wise computation.\n\n If provided an array-like object, the function returns an array-like object\n having the same length and data type as `x`.\n\n If provided an ndarray, the function returns an ndarray having the same\n shape and data type as `x`.\n\n Parameters\n ----------\n x: ndarray|ArrayLikeObject|number\n Input value.\n\n options: Object (optional)\n Options.\n\n options.order: string (optional)\n Output array order (either row-major (C-style) or column-major (Fortran-\n style)). Only applicable when the input array is an ndarray. By default,\n the output array order is inferred from the input array.\n\n options.dtype: string (optional)\n Output array data type. Only applicable when the input array is either\n an ndarray or array-like object. By default, the output array data type\n is inferred from the input array.\n\n Returns\n -------\n y: ndarray|ArrayLikeObject|number\n Results.\n\n Examples\n --------\n // Provide a number:\n > var y = abs( -1.0 )\n 1.0\n\n // Provide an array-like object:\n > var x = new Float64Array( [ -1.0, -2.0 ] );\n > y = abs( x )\n [ 1.0, 2.0 ]\n\n > x = [ -1.0, -2.0 ];\n > y = abs( x )\n [ 1.0, 2.0 ]\n\n // Provide an ndarray:\n > x = array( [ [ -1.0, -2.0 ], [ -3.0, -4.0 ] ] );\n > y = abs( x )\n \n > y.get( 0, 1 )\n 2.0\n\n\nabs.assign( x, y )\n Computes the absolute value and assigns results to a provided output array.\n\n Parameters\n ----------\n x: ndarray|ArrayLikeObject\n Input array.\n\n y: ndarray|ArrayLikeObject\n Output array. Must be the same data \"kind\" (i.e., ndarray or array-like\n object) as the input array.\n\n Returns\n -------\n y: ndarray|ArrayLikeObject\n Output array.\n\n Examples\n --------\n // Provide an array-like object:\n > var x = new Float64Array( [ -1.0, -2.0 ] );\n > var y = new Float64Array( x.length );\n > var out = abs.assign( x, y )\n [ 1.0, 2.0 ]\n > var bool = ( out === y )\n true\n\n > x = [ -1.0, -2.0 ];\n > y = [ 0.0, 0.0 ];\n > out = abs.assign( x, y )\n [ 1.0, 2.0 ]\n > bool = ( out === y )\n true\n\n // Provide an ndarray:\n > x = array( [ [ -1.0, -2.0 ], [ -3.0, -4.0 ] ] );\n > y = array( [ [ 0.0, 0.0 ], [ 0.0, 0.0 ] ] );\n > out = abs.assign( x, y )\n \n > out.get( 0, 1 )\n 2.0\n > bool = ( out === y )\n true\n\n","abs.assign":"\nabs.assign( x, y )\n Computes the absolute value and assigns results to a provided output array.\n\n Parameters\n ----------\n x: ndarray|ArrayLikeObject\n Input array.\n\n y: ndarray|ArrayLikeObject\n Output array. Must be the same data \"kind\" (i.e., ndarray or array-like\n object) as the input array.\n\n Returns\n -------\n y: ndarray|ArrayLikeObject\n Output array.\n\n Examples\n --------\n // Provide an array-like object:\n > var x = new Float64Array( [ -1.0, -2.0 ] );\n > var y = new Float64Array( x.length );\n > var out = abs.assign( x, y )\n [ 1.0, 2.0 ]\n > var bool = ( out === y )\n true\n\n > x = [ -1.0, -2.0 ];\n > y = [ 0.0, 0.0 ];\n > out = abs.assign( x, y )\n [ 1.0, 2.0 ]\n > bool = ( out === y )\n true\n\n // Provide an ndarray:\n > x = array( [ [ -1.0, -2.0 ], [ -3.0, -4.0 ] ] );\n > y = array( [ [ 0.0, 0.0 ], [ 0.0, 0.0 ] ] );\n > out = abs.assign( x, y )\n \n > out.get( 0, 1 )\n 2.0\n > bool = ( out === y )\n true","acartesianPower":"\nacartesianPower( x, n )\n Returns the Cartesian power.\n\n If provided an empty array, the function returns an empty array.\n\n If `n` is less than or equal to zero, the function returns an empty array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n n: integer\n Power.\n\n Returns\n -------\n out: Array\n Cartesian product.\n\n Examples\n --------\n > var x = [ 1, 2 ];\n > var out = acartesianPower( x, 2 )\n [ [ 1, 1 ], [ 1, 2 ], [ 2, 1 ], [ 2, 2 ] ]\n\n See Also\n --------\n acartesianProduct, acartesianSquare\n","acartesianProduct":"\nacartesianProduct( x1, x2 )\n Returns the Cartesian product.\n\n If provided one or more empty arrays, the function returns an empty array.\n\n Parameters\n ----------\n x1: ArrayLikeObject\n First input array.\n\n x2: ArrayLikeObject\n Second input array.\n\n Returns\n -------\n out: Array\n Cartesian product.\n\n Examples\n --------\n > var x1 = [ 1, 2 ];\n > var x2 = [ 3, 4 ];\n > var out = acartesianProduct( x1, x2 )\n [ [ 1, 3 ], [ 1, 4 ], [ 2, 3 ], [ 2, 4 ] ]\n\n See Also\n --------\n acartesianPower, acartesianSquare\n","acartesianSquare":"\nacartesianSquare( x )\n Returns the Cartesian square.\n\n If provided an empty array, the function returns an empty array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n Returns\n -------\n out: Array\n Cartesian product.\n\n Examples\n --------\n > var out = acartesianSquare( [ 1, 2 ] )\n [ [ 1, 1 ], [ 1, 2 ], [ 2, 1 ], [ 2, 2 ] ]\n\n See Also\n --------\n acartesianPower, acartesianProduct\n","acronym":"\nacronym( str[, options] )\n Generates an acronym for a given string.\n\n Parameters\n ----------\n str: string\n Input string.\n\n options: Object (optional)\n Options.\n\n options.stopwords: Array (optional)\n Array of custom stop words.\n\n Returns\n -------\n out: string\n Acronym for the given string.\n\n Examples\n --------\n > var out = acronym( 'the quick brown fox' )\n 'QBF'\n > out = acronym( 'Hard-boiled eggs' )\n 'HBE'\n","aempty":"\naempty( length[, dtype] )\n Creates an uninitialized array having a specified length.\n\n In browser environments, the function always returns zero-filled arrays.\n\n If `dtype` is 'generic', the function always returns a zero-filled array.\n\n In Node.js versions >=3.0.0, the underlying memory of returned typed arrays\n is *not* initialized. Memory contents are unknown and may contain\n *sensitive* data.\n\n Parameters\n ----------\n length: integer\n Array length.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = aempty( 2 )\n \n > arr = aempty( 2, 'float32' )\n \n\n See Also\n --------\n aemptyLike, afull, aones, azeros, ndempty\n","aemptyLike":"\naemptyLike( x[, dtype] )\n Creates an uninitialized array having the same length and data type as a\n provided input array.\n\n In browser environments, the function always returns zero-filled arrays.\n\n If `dtype` is 'generic', the function always returns a zero-filled array.\n\n In Node.js versions >=3.0.0, the underlying memory of returned typed arrays\n is *not* initialized. Memory contents are unknown and may contain\n *sensitive* data.\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var x = new Float64Array( 2 );\n > var arr = aemptyLike( x )\n \n > arr = aemptyLike( x, 'float32' )\n \n\n See Also\n --------\n aempty, afullLike, aonesLike, azerosLike, ndemptyLike\n","AFINN_96":"\nAFINN_96()\n Returns a list of English words rated for valence.\n\n The returned list contains 1468 English words (and phrases) rated for\n valence. Negative words have a negative valence [-5,0). Positive words have\n a positive valence (0,5]. Neutral words have a valence of 0.\n\n A few notes:\n\n - The list is an earlier version of AFINN-111.\n - The list includes misspelled words. Their presence is intentional, as such\n misspellings frequently occur in social media content.\n - All words are lowercase.\n - Some \"words\" are phrases; e.g., \"cashing in\", \"cool stuff\".\n - Words may contain apostrophes; e.g., \"can't stand\".\n - Words may contain dashes; e.g., \"cover-up\", \"made-up\".\n\n Returns\n -------\n out: Array\n List of English words and their valence.\n\n Examples\n --------\n > var list = AFINN_96()\n [ [ 'abandon', -2 ], [ 'abandons', -2 ], [ 'abandoned', -2 ], ... ]\n\n References\n ----------\n - Nielsen, Finn Årup. 2011. \"A new ANEW: Evaluation of a word list for\n sentiment analysis in microblogs.\" In *Proceedings of the ESWC2011 Workshop\n on 'Making Sense of Microposts': Big Things Come in Small Packages.*,\n 718:93–98. CEUR Workshop Proceedings. .\n\n * If you use the list for publication or third party consumption, please\n cite the listed reference.\n\n See Also\n --------\n AFINN_111\n","AFINN_111":"\nAFINN_111()\n Returns a list of English words rated for valence.\n\n The returned list contains 2477 English words (and phrases) rated for\n valence. Negative words have a negative valence [-5,0). Positive words have\n a positive valence (0,5]. Neutral words have a valence of 0.\n\n A few notes:\n\n - The list includes misspelled words. Their presence is intentional, as such\n misspellings frequently occur in social media content.\n - All words are lowercase.\n - Words may contain numbers; e.g., \"n00b\".\n - Some \"words\" are phrases; e.g., \"cool stuff\", \"not good\".\n - Words may contain apostrophes; e.g., \"can't stand\".\n - Words may contain diaeresis; e.g., \"naïve\".\n - Words may contain dashes; e.g., \"self-deluded\", \"self-confident\".\n\n Returns\n -------\n out: Array\n List of English words and their valence.\n\n Examples\n --------\n > var list = AFINN_111()\n [ [ 'abandon', -2 ], [ 'abandoned', -2 ], [ 'abandons', -2 ], ... ]\n\n References\n ----------\n - Nielsen, Finn Årup. 2011. \"A new ANEW: Evaluation of a word list for\n sentiment analysis in microblogs.\" In *Proceedings of the ESWC2011 Workshop\n on 'Making Sense of Microposts': Big Things Come in Small Packages.*,\n 718:93–98. CEUR Workshop Proceedings. .\n\n * If you use the list for publication or third party consumption, please\n cite the listed reference.\n\n See Also\n --------\n AFINN_96\n","afull":"\nafull( length, value[, dtype] )\n Returns a filled array having a specified length.\n\n Parameters\n ----------\n length: integer\n Array length.\n\n value: any\n Fill value.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = afull( 2, 1.0 )\n [ 1.0, 1.0 ]\n > arr = afull( 2, 1.0, 'float32' )\n [ 1.0, 1.0 ]\n\n See Also\n --------\n afullLike, aones, azeros\n","afullLike":"\nafullLike( x[, dtype] )\n Returns a filled array having the same length and data type as a provided\n input array.\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var x = new Float64Array( 2 );\n > var y = afullLike( x, 1.0 )\n [ 1.0, 1.0 ]\n > y = afullLike( x, 1.0, 'float32' )\n [ 1.0, 1.0 ]\n\n See Also\n --------\n afull, aonesLike, azerosLike\n","alias2pkg":"\nalias2pkg( alias )\n Returns the package name associated with a provided alias.\n\n Parameters\n ----------\n alias: string\n Alias.\n\n Returns\n -------\n out: string|null\n Package name.\n\n Examples\n --------\n > var v = alias2pkg( 'base.sin' )\n '@stdlib/math/base/special/sin'\n\n See Also\n --------\n alias2related, aliases, pkg2alias\n","alias2related":"\nalias2related( alias )\n Returns aliases related to a specified alias.\n\n Parameters\n ----------\n alias: string\n Alias.\n\n Returns\n -------\n out: Array|null\n Related aliases.\n\n Examples\n --------\n > var v = alias2related( 'base.sin' )\n [...]\n\n See Also\n --------\n alias2pkg, aliases, pkg2related\n","alias2standalone":"\nalias2standalone( alias )\n Returns the standalone package name associated with a provided alias.\n\n Parameters\n ----------\n alias: string\n Alias.\n\n Returns\n -------\n out: string|null\n Standalone package name.\n\n Examples\n --------\n > var v = alias2standalone( 'base.sin' )\n '@stdlib/math-base-special-sin'\n\n See Also\n --------\n alias2pkg, alias2related, aliases, pkg2alias, pkg2standalone\n","aliases":"\naliases( [namespace] )\n Returns a list of standard library aliases.\n\n Parameters\n ----------\n namespace: string (optional)\n Namespace filter.\n\n Returns\n -------\n out: Array\n List of aliases.\n\n Examples\n --------\n > var o = aliases()\n [...]\n > o = aliases( '@stdlib/math/base/special' )\n [...]\n\n See Also\n --------\n alias2pkg, alias2related, pkg2alias\n","allocUnsafe":"\nallocUnsafe( size )\n Allocates a buffer having a specified number of bytes.\n\n The underlying memory of returned buffers is not initialized. Memory\n contents are unknown and may contain sensitive data.\n\n When the size is less than half a buffer pool size, memory is allocated from\n the buffer pool for faster allocation of Buffer instances.\n\n Parameters\n ----------\n size: integer\n Number of bytes to allocate.\n\n Returns\n -------\n out: Buffer\n Buffer instance.\n\n Examples\n --------\n > var buf = allocUnsafe( 100 )\n \n\n See Also\n --------\n Buffer, array2buffer, arraybuffer2buffer, copyBuffer, string2buffer\n","amskfilter":"\namskfilter( x, mask )\n Returns a new array by applying a mask to a provided input array.\n\n If a mask array element is truthy, the corresponding element in `x` is\n included in the output array; otherwise, the corresponding element in `x` is\n \"masked\" and thus excluded from the output array.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n mask: Array|TypedArray|Object\n Mask array.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Output array.\n\n Examples\n --------\n > var x = [ 1, 2, 3, 4 ];\n > var y = amskfilter( x, [ 0, 1, 0, 1 ] )\n [ 2, 4 ]\n\n See Also\n --------\n amskreject\n","amskput":"\namskput( x, mask, values[, options] )\n Replaces elements of an array with provided values according to a provided\n mask array.\n\n In broadcasting modes, the function supports broadcasting a values array\n containing a single element against the number of falsy values in the mask\n array.\n\n In repeat mode, the function supports recycling elements in a values array\n to satisfy the number of falsy values in the mask array.\n\n The function mutates the input array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n mask: ArrayLikeObject\n Mask array. If a mask array element is falsy, the corresponding element\n in `x` is *replaced*; otherwise, the corresponding element in `x` is\n \"masked\" and thus left unchanged.\n\n values: ArrayLikeObject\n Values to set.\n\n options: Object (optional)\n Function options.\n\n options.mode: string (optional)\n String specifying behavior when the number of values to set does not\n equal the number of falsy mask values. The function supports the\n following modes:\n\n - 'strict': specifies that the function must raise an exception when the\n number of values does not *exactly* equal the number of falsy mask\n values.\n - 'non_strict': specifies that the function must raise an exception when\n the function is provided insufficient values to satisfy the mask array.\n - 'strict_broadcast': specifies that the function must broadcast a\n single-element values array and otherwise raise an exception when the\n number of values does not **exactly** equal the number of falsy mask\n values.\n - 'broadcast': specifies that the function must broadcast a single-\n element values array and otherwise raise an exception when the function\n is provided insufficient values to satisfy the mask array.\n - 'repeat': specifies that the function must reuse provided values when\n replacing elements in `x` in order to satisfy the mask array.\n\n Default: 'repeat'.\n\n Returns\n -------\n out: ArrayLikeObject\n Input array.\n\n Examples\n --------\n > var x = [ 1, 2, 3, 4 ];\n > var out = amskput( x, [ 1, 0, 1, 0 ], [ 20, 40 ] )\n [ 1, 20, 3, 40 ]\n > var bool = ( out === x )\n true\n\n See Also\n --------\n aplace, aput, atake\n","amskreject":"\namskreject( x, mask )\n Returns a new array by applying a mask to a provided input array.\n\n If a mask array element is falsy, the corresponding element in `x` is\n included in the output array; otherwise, the corresponding element in `x` is\n \"masked\" and thus excluded from the output array.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n mask: Array|TypedArray|Object\n Mask array.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Output array.\n\n Examples\n --------\n > var x = [ 1, 2, 3, 4 ];\n > var y = amskreject( x, [ 0, 1, 0, 1 ] )\n [ 1, 3 ]\n\n See Also\n --------\n amskfilter\n","anans":"\nanans( length[, dtype] )\n Returns an array filled with NaNs and having a specified length.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754)\n - float32: single-precision floating-point numbers (IEEE 754)\n - complex128: double-precision complex floating-point numbers\n - complex64: single-precision complex floating-point numbers\n - generic: generic JavaScript values\n\n The default array data type is `float64`.\n\n Parameters\n ----------\n length: integer\n Array length.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = anans( 2 )\n [ NaN, NaN ]\n > arr = anans( 2, 'float32' )\n [ NaN, NaN ]\n\n See Also\n --------\n afull, anansLike, aones, azeros\n","anansLike":"\nanansLike( x[, dtype] )\n Returns an array filled with NaNs and having the same length and data type\n as a provided input array.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754).\n - float32: single-precision floating-point numbers (IEEE 754).\n - complex128: double-precision complex floating-point numbers.\n - complex64: single-precision complex floating-point numbers.\n - generic: generic JavaScript values.\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var x = new Float64Array( 2 );\n > var y = anansLike( x )\n [ NaN, NaN ]\n > y = anansLike( x, 'float32' )\n [ NaN, NaN ]\n\n See Also\n --------\n afullLike, anans, aonesLike, azerosLike\n","anova1":"\nanova1( x, factor[, options] )\n Performs a one-way analysis of variance.\n\n Parameters\n ----------\n x: Array\n Measured values.\n\n factor: Array\n Array of treatments.\n\n options: Object (optional)\n Options.\n\n options.alpha: number (optional)\n Number in the interval `[0,1]` giving the significance level of the\n hypothesis test. Default: `0.05`.\n\n Returns\n -------\n out: Object\n Test result object.\n\n out.alpha: number\n Significance level.\n\n out.rejected: boolean\n Test decision.\n\n out.pValue: number\n p-value of the test.\n\n out.statistic: number\n Value of test statistic.\n\n out.method: string\n Name of test.\n\n out.means: Object\n Group means alongside sample sizes and standard errors.\n\n out.treatment: Object\n Treatment results.\n\n out.treatment.df: number\n Treatment degrees of freedom.\n\n out.treatment.ss: number\n Treatment sum of squares.\n\n out.treatment.ms: number\n Treatment mean sum of squares.\n\n out.error: Object\n Error results.\n\n out.error.df: number\n Error degrees of freedom.\n\n out.error.ss: number\n Error sum of squares.\n\n out.error.ms: number\n Error mean sum of squares.\n\n out.print: Function\n Function to print formatted output.\n\n Examples\n --------\n > var x = [1, 3, 5, 2, 4, 6, 8, 7, 10, 11, 12, 15];\n > var f = [\n ... 'control', 'treatA', 'treatB', 'treatC', 'control',\n ... 'treatA', 'treatB', 'treatC', 'control', 'treatA', 'treatB', 'treatC'\n ... ];\n > var out = anova1( x, f )\n {...}\n\n","ANSCOMBES_QUARTET":"\nANSCOMBES_QUARTET()\n Returns Anscombe's quartet.\n\n Anscombe's quartet is a set of 4 datasets which all have nearly identical\n simple statistical properties but vary considerably when graphed. Anscombe\n created the datasets to demonstrate why graphical data exploration should\n precede statistical data analysis and to show the effect of outliers on\n statistical properties.\n\n Returns\n -------\n out: Array\n Anscombe's quartet.\n\n Examples\n --------\n > var d = ANSCOMBES_QUARTET()\n [[[10,8.04],...],[[10,9.14],...],[[10,7.46],...],[[8,6.58],...]]\n\n References\n ----------\n - Anscombe, Francis J. 1973. \"Graphs in Statistical Analysis.\" *The American\n Statistician* 27 (1). [American Statistical Association, Taylor & Francis,\n Ltd.]: 17–21. .\n\n","any":"\nany( collection )\n Tests whether at least one element in a collection is truthy.\n\n The function immediately returns upon encountering a truthy value.\n\n If provided an empty collection, the function returns `false`.\n\n Parameters\n ----------\n collection: Array|TypedArray|Object\n Input collection over which to iterate. If provided an object, the\n object must be array-like (excluding strings and functions).\n\n Returns\n -------\n bool: boolean\n The function returns `true` if an element is truthy; otherwise, the\n function returns `false`.\n\n Examples\n --------\n > var arr = [ 0, 0, 0, 0, 1 ];\n > var bool = any( arr )\n true\n\n See Also\n --------\n anyBy, every, forEach, none, some\n","anyBy":"\nanyBy( collection, predicate[, thisArg ] )\n Tests whether at least one element in a collection passes a test implemented\n by a predicate function.\n\n The predicate function is provided three arguments:\n\n - value: collection value.\n - index: collection index.\n - collection: the input collection.\n\n The function immediately returns upon encountering a truthy return value.\n\n If provided an empty collection, the function returns `false`.\n\n Parameters\n ----------\n collection: Array|TypedArray|Object\n Input collection over which to iterate. If provided an object, the\n object must be array-like (excluding strings and functions).\n\n predicate: Function\n The test function.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n bool: boolean\n The function returns `true` if the predicate function returns `true` for\n any element; otherwise, the function returns `false`.\n\n Examples\n --------\n > function negative( v ) { return ( v < 0 ); };\n > var arr = [ 1, 2, 3, 4, -1 ];\n > var bool = anyBy( arr, negative )\n true\n\n See Also\n --------\n anyByAsync, anyByRight, everyBy, forEach, noneBy, someBy\n","anyByAsync":"\nanyByAsync( collection, [options,] predicate, done )\n Tests whether at least one element in a collection passes a test implemented\n by a predicate function.\n\n When invoked, the predicate function is provided a maximum of four\n arguments:\n\n - value: collection value.\n - index: collection index.\n - collection: the input collection.\n - next: a callback to be invoked after processing a collection `value`.\n\n The actual number of provided arguments depends on function length. If the\n predicate function accepts two arguments, the predicate function is\n provided:\n\n - value\n - next\n\n If the predicate function accepts three arguments, the predicate function is\n provided:\n\n - value\n - index\n - next\n\n For every other predicate function signature, the predicate function is\n provided all four arguments.\n\n The `next` callback takes two arguments:\n\n - error: error argument.\n - result: test result.\n\n If a provided function calls the `next` callback with a truthy `error`\n argument, the function suspends execution and immediately calls the `done`\n callback for subsequent `error` handling.\n\n The function immediately returns upon encountering a non-falsy `result`\n value and calls the `done` callback with `null` as the first argument and\n `true` as the second argument.\n\n If all elements fail, the function calls the `done` callback with `null`\n as the first argument and `false` as the second argument.\n\n Execution is *not* guaranteed to be asynchronous. To guarantee asynchrony,\n wrap the `done` callback in a function which either executes at the end of\n the current stack (e.g., `nextTick`) or during a subsequent turn of the\n event loop (e.g., `setImmediate`, `setTimeout`).\n\n The function does not support dynamic collection resizing.\n\n The function does not skip `undefined` elements.\n\n Parameters\n ----------\n collection: Array|TypedArray|Object\n Input collection over which to iterate. If provided an object, the\n object must be array-like (excluding strings and functions).\n\n options: Object (optional)\n Function options.\n\n options.limit: integer (optional)\n Maximum number of pending invocations. Default: Infinity.\n\n options.series: boolean (optional)\n Boolean indicating whether to process each collection element\n sequentially. Default: false.\n\n options.thisArg: any (optional)\n Execution context.\n\n predicate: Function\n The test function to invoke for each element in a collection.\n\n done: Function\n A callback invoked either upon processing all collection elements or\n upon encountering an error.\n\n Examples\n --------\n // Basic usage:\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var arr = [ 3000, 2500, 1000 ];\n > anyByAsync( arr, predicate, done )\n 1000\n 2500\n 3000\n false\n\n // Limit number of concurrent invocations:\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var opts = { 'limit': 2 };\n > var arr = [ 3000, 2500, 1000 ];\n > anyByAsync( arr, opts, predicate, done )\n 2500\n 3000\n 1000\n false\n\n // Process sequentially:\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var opts = { 'series': true };\n > var arr = [ 3000, 2500, 1000 ];\n > anyByAsync( arr, opts, predicate, done )\n 3000\n 2500\n 1000\n false\n\n\nanyByAsync.factory( [options,] predicate )\n Returns a function which tests whether at least one element in a collection\n passes a test implemented by a predicate function.\n\n Parameters\n ----------\n options: Object (optional)\n Function options.\n\n options.limit: integer (optional)\n Maximum number of pending invocations. Default: Infinity.\n\n options.series: boolean (optional)\n Boolean indicating whether to process each collection element\n sequentially. Default: false.\n\n options.thisArg: any (optional)\n Execution context.\n\n predicate: Function\n The test function to invoke for each element in a collection.\n\n Returns\n -------\n out: Function\n A function which tests each element in a collection.\n\n Examples\n --------\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > var opts = { 'series': true };\n > var f = anyByAsync.factory( opts, predicate );\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var arr = [ 3000, 2500, 1000 ];\n > f( arr, done )\n 3000\n 2500\n 1000\n false\n > arr = [ 2000, 1500, 1000 ];\n > f( arr, done )\n 2000\n 1500\n 1000\n false\n\n See Also\n --------\n anyBy, anyByRightAsync, everyByAsync, forEachAsync, noneByAsync, someByAsync\n","anyByAsync.factory":"\nanyByAsync.factory( [options,] predicate )\n Returns a function which tests whether at least one element in a collection\n passes a test implemented by a predicate function.\n\n Parameters\n ----------\n options: Object (optional)\n Function options.\n\n options.limit: integer (optional)\n Maximum number of pending invocations. Default: Infinity.\n\n options.series: boolean (optional)\n Boolean indicating whether to process each collection element\n sequentially. Default: false.\n\n options.thisArg: any (optional)\n Execution context.\n\n predicate: Function\n The test function to invoke for each element in a collection.\n\n Returns\n -------\n out: Function\n A function which tests each element in a collection.\n\n Examples\n --------\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > var opts = { 'series': true };\n > var f = anyByAsync.factory( opts, predicate );\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var arr = [ 3000, 2500, 1000 ];\n > f( arr, done )\n 3000\n 2500\n 1000\n false\n > arr = [ 2000, 1500, 1000 ];\n > f( arr, done )\n 2000\n 1500\n 1000\n false\n\n See Also\n --------\n anyBy, anyByRightAsync, everyByAsync, forEachAsync, noneByAsync, someByAsync","anyByRight":"\nanyByRight( collection, predicate[, thisArg ] )\n Tests whether at least one element in a collection passes a test implemented\n by a predicate function, iterating from right to left.\n\n The predicate function is provided three arguments:\n\n - value: collection value.\n - index: collection index.\n - collection: the input collection.\n\n The function immediately returns upon encountering a truthy return value.\n\n If provided an empty collection, the function returns `false`.\n\n Parameters\n ----------\n collection: Array|TypedArray|Object\n Input collection over which to iterate. If provided an object, the\n object must be array-like (excluding strings and functions).\n\n predicate: Function\n The test function.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n bool: boolean\n The function returns `true` if the predicate function returns `true` for\n any element; otherwise, the function returns `false`.\n\n Examples\n --------\n > function negative( v ) { return ( v < 0 ); };\n > var arr = [ -1, 1, 2, 3, 4 ];\n > var bool = anyByRight( arr, negative )\n true\n\n See Also\n --------\n anyBy, anyByRightAsync, everyByRight, forEachRight, noneByRight, someByRight\n","anyByRightAsync":"\nanyByRightAsync( collection, [options,] predicate, done )\n Tests whether at least one element in a collection passes a test implemented\n by a predicate function, iterating from right to left.\n\n When invoked, the predicate function is provided a maximum of four\n arguments:\n\n - value: collection value.\n - index: collection index.\n - collection: the input collection.\n - next: a callback to be invoked after processing a collection `value`.\n\n The actual number of provided arguments depends on function length. If the\n predicate function accepts two arguments, the predicate function is\n provided:\n\n - value\n - next\n\n If the predicate function accepts three arguments, the predicate function is\n provided:\n\n - value\n - index\n - next\n\n For every other predicate function signature, the predicate function is\n provided all four arguments.\n\n The `next` callback takes two arguments:\n\n - error: error argument.\n - result: test result.\n\n If a provided function calls the `next` callback with a truthy `error`\n argument, the function suspends execution and immediately calls the `done`\n callback for subsequent `error` handling.\n\n The function immediately returns upon encountering a non-falsy `result`\n value and calls the `done` callback with `null` as the first argument and\n `true` as the second argument.\n\n If all elements fail, the function calls the `done` callback with `null`\n as the first argument and `false` as the second argument.\n\n Execution is *not* guaranteed to be asynchronous. To guarantee asynchrony,\n wrap the `done` callback in a function which either executes at the end of\n the current stack (e.g., `nextTick`) or during a subsequent turn of the\n event loop (e.g., `setImmediate`, `setTimeout`).\n\n The function does not support dynamic collection resizing.\n\n The function does not skip `undefined` elements.\n\n Parameters\n ----------\n collection: Array|TypedArray|Object\n Input collection over which to iterate. If provided an object, the\n object must be array-like (excluding strings and functions).\n\n options: Object (optional)\n Function options.\n\n options.limit: integer (optional)\n Maximum number of pending invocations. Default: Infinity.\n\n options.series: boolean (optional)\n Boolean indicating whether to process each collection element\n sequentially. Default: false.\n\n options.thisArg: any (optional)\n Execution context.\n\n predicate: Function\n The test function to invoke for each element in a collection.\n\n done: Function\n A callback invoked either upon processing all collection elements or\n upon encountering an error.\n\n Examples\n --------\n // Basic usage:\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var arr = [ 1000, 2500, 3000 ];\n > anyByRightAsync( arr, predicate, done )\n 1000\n 2500\n 3000\n false\n\n // Limit number of concurrent invocations:\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var opts = { 'limit': 2 };\n > var arr = [ 1000, 2500, 3000 ];\n > anyByRightAsync( arr, opts, predicate, done )\n 2500\n 3000\n 1000\n false\n\n // Process sequentially:\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var opts = { 'series': true };\n > var arr = [ 1000, 2500, 3000 ];\n > anyByRightAsync( arr, opts, predicate, done )\n 3000\n 2500\n 1000\n false\n\n\nanyByRightAsync.factory( [options,] predicate )\n Returns a function which tests whether at least one element in a collection\n passes a test implemented by a predicate function, iterating from right to\n left.\n\n Parameters\n ----------\n options: Object (optional)\n Function options.\n\n options.limit: integer (optional)\n Maximum number of pending invocations. Default: Infinity.\n\n options.series: boolean (optional)\n Boolean indicating whether to process each collection element\n sequentially. Default: false.\n\n options.thisArg: any (optional)\n Execution context.\n\n predicate: Function\n The test function to invoke for each element in a collection.\n\n Returns\n -------\n out: Function\n A function which tests each element in a collection.\n\n Examples\n --------\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > var opts = { 'series': true };\n > var f = anyByRightAsync.factory( opts, predicate );\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var arr = [ 1000, 2500, 3000 ];\n > f( arr, done )\n 3000\n 2500\n 1000\n false\n > arr = [ 1000, 1500, 2000 ];\n > f( arr, done )\n 2000\n 1500\n 1000\n false\n\n See Also\n --------\n anyByAsync, anyByRight, everyByRightAsync, forEachRightAsync, noneByRightAsync, someByRightAsync\n","anyByRightAsync.factory":"\nanyByRightAsync.factory( [options,] predicate )\n Returns a function which tests whether at least one element in a collection\n passes a test implemented by a predicate function, iterating from right to\n left.\n\n Parameters\n ----------\n options: Object (optional)\n Function options.\n\n options.limit: integer (optional)\n Maximum number of pending invocations. Default: Infinity.\n\n options.series: boolean (optional)\n Boolean indicating whether to process each collection element\n sequentially. Default: false.\n\n options.thisArg: any (optional)\n Execution context.\n\n predicate: Function\n The test function to invoke for each element in a collection.\n\n Returns\n -------\n out: Function\n A function which tests each element in a collection.\n\n Examples\n --------\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > var opts = { 'series': true };\n > var f = anyByRightAsync.factory( opts, predicate );\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var arr = [ 1000, 2500, 3000 ];\n > f( arr, done )\n 3000\n 2500\n 1000\n false\n > arr = [ 1000, 1500, 2000 ];\n > f( arr, done )\n 2000\n 1500\n 1000\n false\n\n See Also\n --------\n anyByAsync, anyByRight, everyByRightAsync, forEachRightAsync, noneByRightAsync, someByRightAsync","anyInBy":"\nanyInBy( object, predicate[, thisArg ] )\n Tests whether at least one value in an object passes a test implemented by\n a predicate function.\n\n The predicate function is provided three arguments:\n\n - value: the value of the current property being processed in the object\n - key: the key of the current property being processed in the object\n - object: the input object\n\n The function immediately returns upon encountering a truthy return value.\n\n If provided an empty object, the function returns `false`.\n\n Parameters\n ----------\n object: Object\n Input object over which to iterate. It must be non-null.\n\n predicate: Function\n The test function.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n bool: boolean\n The function returns `true` if the predicate function returns `true` for\n any value; otherwise, it returns `false`.\n\n Examples\n --------\n > function isNegative(value) { return value < 0 }\n > var obj = { a: 1, b: -2, c: 3, d: 4 }\n > var result = anyInBy(obj, isNegative)\n true\n\n See Also\n --------\n anyBy, anyOwnBy, everyInBy, someInBy","anyOwnBy":"\nanyOwnBy( object, predicate[, thisArg ] )\n Tests whether at least one own property of an object passes a\n test implemented by a predicate function.\n\n The predicate function is provided three arguments:\n\n - value: property value.\n - index: property key.\n - object: the input object.\n\n The function immediately returns upon encountering a truthy return\n value.\n\n If provided an empty object, the function returns `false`.\n\n Parameters\n ----------\n object: Object\n Input object.\n\n predicate: Function\n Test function.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n bool: boolean\n The function returns `true` if the predicate function returns a truthy\n value for one own property; otherwise, the function returns `false`.\n\n Examples\n --------\n > function positive( v ) { return ( v > 0 ); };\n > var obj = { 'a': -1, 'b': 2, 'c': -3 };\n > var bool = anyOwnBy( obj, positive )\n true\n\n See Also\n --------\n anyBy, anyInBy, everyOwnBy, someOwnBy\n","aones":"\naones( length[, dtype] )\n Returns an array filled with ones and having a specified length.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754).\n - float32: single-precision floating-point numbers (IEEE 754).\n - complex128: double-precision complex floating-point numbers.\n - complex64: single-precision complex floating-point numbers.\n - int32: 32-bit two's complement signed integers.\n - uint32: 32-bit unsigned integers.\n - int16: 16-bit two's complement signed integers.\n - uint16: 16-bit unsigned integers.\n - int8: 8-bit two's complement signed integers.\n - uint8: 8-bit unsigned integers.\n - uint8c: 8-bit unsigned integers clamped to 0-255.\n - generic: generic JavaScript values.\n\n The default array data type is `float64`.\n\n Parameters\n ----------\n length: integer\n Array length.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = aones( 2 )\n [ 1.0, 1.0 ]\n > arr = aones( 2, 'float32' )\n [ 1.0, 1.0 ]\n\n See Also\n --------\n afull, anans, aonesLike, azeros\n","aonesLike":"\naonesLike( x[, dtype] )\n Returns an array filled with ones and having the same length and data type\n as a provided input array.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754).\n - float32: single-precision floating-point numbers (IEEE 754).\n - complex128: double-precision complex floating-point numbers.\n - complex64: single-precision complex floating-point numbers.\n - int32: 32-bit two's complement signed integers.\n - uint32: 32-bit unsigned integers.\n - int16: 16-bit two's complement signed integers.\n - uint16: 16-bit unsigned integers.\n - int8: 8-bit two's complement signed integers.\n - uint8: 8-bit unsigned integers.\n - uint8c: 8-bit unsigned integers clamped to 0-255.\n - generic: generic JavaScript values.\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var x = new Float64Array( 2 );\n > var y = aonesLike( x )\n [ 1.0, 1.0 ]\n > y = aonesLike( x, 'float32' )\n [ 1.0, 1.0 ]\n\n See Also\n --------\n afullLike, anansLike, aones, azerosLike\n","aoneTo":"\naoneTo( n[, dtype] )\n Generates a linearly spaced numeric array whose elements increment by 1\n starting from one.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754).\n - float32: single-precision floating-point numbers (IEEE 754).\n - complex128: double-precision complex floating-point numbers.\n - complex64: single-precision complex floating-point numbers.\n - int32: 32-bit two's complement signed integers.\n - uint32: 32-bit unsigned integers.\n - int16: 16-bit two's complement signed integers.\n - uint16: 16-bit unsigned integers.\n - int8: 8-bit two's complement signed integers.\n - uint8: 8-bit unsigned integers.\n - uint8c: 8-bit unsigned integers clamped to 0-255.\n - generic: generic JavaScript values.\n\n The default array data type is `float64`.\n\n If `n` is equal to zero, the function returns an empty array.\n\n Parameters\n ----------\n n: integer\n Number of elements.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = aoneTo( 2 )\n [ 1.0, 2.0 ]\n > arr = aoneTo( 2, 'float32' )\n [ 1.0, 2.0 ]\n\n See Also\n --------\n afull, aones, aoneToLike, azeroTo\n","aoneToLike":"\naoneToLike( x[, dtype] )\n Generates a linearly spaced numeric array whose elements increment by 1\n starting from one and having the same length and data type as a provided\n input array.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754).\n - float32: single-precision floating-point numbers (IEEE 754).\n - complex128: double-precision complex floating-point numbers.\n - complex64: single-precision complex floating-point numbers.\n - int32: 32-bit two's complement signed integers.\n - uint32: 32-bit unsigned integers.\n - int16: 16-bit two's complement signed integers.\n - uint16: 16-bit unsigned integers.\n - int8: 8-bit two's complement signed integers.\n - uint8: 8-bit unsigned integers.\n - uint8c: 8-bit unsigned integers clamped to 0-255.\n - generic: generic JavaScript values.\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = aoneToLike( [ 0, 0 ] )\n [ 1, 2 ]\n > arr = aoneToLike( [ 0, 0 ], 'float32' )\n [ 1.0, 2.0 ]\n\n See Also\n --------\n afullLike, aonesLike, aoneTo, azeroToLike\n","APERY":"\nAPERY\n Apéry's constant.\n\n Examples\n --------\n > APERY\n 1.2020569031595942\n\n","aplace":"\naplace( x, mask, values[, options] )\n Replaces elements of an array with provided values according to a provided\n mask array.\n\n In broadcasting modes, the function supports broadcasting a values array\n containing a single element against the number of truthy values in the mask\n array.\n\n In repeat mode, the function supports recycling elements in a values array\n to satisfy the number of truthy values in the mask array.\n\n The function mutates the input array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n mask: ArrayLikeObject\n Mask array. If a mask array element is truthy, the corresponding element\n in `x` is *replaced*; otherwise, the corresponding element in `x` is\n \"masked\" and thus left unchanged.\n\n values: ArrayLikeObject\n Values to set.\n\n options: Object (optional)\n Function options.\n\n options.mode: string (optional)\n String specifying behavior when the number of values to set does not\n equal the number of truthy mask values. The function supports the\n following modes:\n\n - 'strict': specifies that the function must raise an exception when the\n number of values does not *exactly* equal the number of truthy mask\n values.\n - 'non_strict': specifies that the function must raise an exception when\n the function is provided insufficient values to satisfy the mask array.\n - 'strict_broadcast': specifies that the function must broadcast a\n single-element values array and otherwise raise an exception when the\n number of values does not **exactly** equal the number of truthy mask\n values.\n - 'broadcast': specifies that the function must broadcast a single-\n element values array and otherwise raise an exception when the function\n is provided insufficient values to satisfy the mask array.\n - 'repeat': specifies that the function must reuse provided values when\n replacing elements in `x` in order to satisfy the mask array.\n\n Default: 'repeat'.\n\n Returns\n -------\n out: ArrayLikeObject\n Input array.\n\n Examples\n --------\n > var x = [ 1, 2, 3, 4 ];\n > var out = aplace( x, [ 0, 1, 0, 1 ], [ 20, 40 ] )\n [ 1, 20, 3, 40 ]\n > var bool = ( out === x )\n true\n\n See Also\n --------\n amskput, aput, atake\n","append":"\nappend( collection1, collection2 )\n Adds the elements of one collection to the end of another collection.\n\n If the input collection is a typed array, the output value does not equal\n the input reference and the underlying `ArrayBuffer` may *not* be the same\n as the `ArrayBuffer` belonging to the input view.\n\n For purposes of generality, always treat the output collection as distinct\n from the input collection.\n\n Parameters\n ----------\n collection1: Array|TypedArray|Object\n A collection. If the collection is an `Object`, the collection should be\n array-like.\n\n collection2: Array|TypedArray|Object\n A collection containing the elements to add. If the collection is an\n `Object`, the collection should be array-like.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Updated collection.\n\n Examples\n --------\n // Arrays:\n > var arr = [ 1.0, 2.0, 3.0, 4.0, 5.0 ];\n > arr = append( arr, [ 6.0, 7.0 ] )\n [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 ]\n\n // Typed arrays:\n > arr = new Float64Array( [ 1.0, 2.0 ] );\n > arr = append( arr, [ 3.0, 4.0 ] )\n [ 1.0, 2.0, 3.0, 4.0 ]\n\n // Array-like object:\n > arr = { 'length': 0 };\n > arr = append( arr, [ 1.0, 2.0 ] )\n { 'length': 2, '0': 1.0, '1': 2.0 }\n\n See Also\n --------\n prepend, push\n","aput":"\naput( x, indices, values[, options] )\n Replaces specified elements of an array with provided values.\n\n The function supports broadcasting a `values` array containing a single\n element against an `indices` array containing one or more elements.\n\n The function mutates the input array.\n\n Because each index is only validated at the time of replacing a particular\n element, mutation may occur even when one or more indices are out-of-bounds,\n including when the index mode indicates to raise an exception.\n\n If `indices` is an empty array, the function returns the input array\n unchanged.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n indices: ArrayLikeObject\n List of element indices.\n\n values: ArrayLikeObject\n Values to set. When `indices` contains one or more elements, `values`\n must be broadcast compatible with `indices` (i.e., must have either one\n element or the same number of elements as `indices`).\n\n options: Object (optional)\n Function options.\n\n options.mode: string (optional)\n Specifies how to handle an index outside the interval [0, max], where\n `max` is the maximum possible array index. If equal to 'throw', the\n function throws an error. If equal to 'normalize', the function throws\n an error if provided an out-of-bounds normalized index. If equal to\n 'wrap', the function wraps around an index using modulo arithmetic. If\n equal to 'clamp', the function sets an index to either 0 (minimum index)\n or the maximum index. Default: 'normalize'.\n\n Returns\n -------\n out: ArrayLikeObject\n Input array.\n\n Examples\n --------\n > var x = [ 1, 2, 3, 4 ];\n > var out = aput( x, [ 1, 3 ], [ 20, 40 ] )\n [ 1, 20, 3, 40 ]\n > var bool = ( out === x )\n true\n\n See Also\n --------\n amskput, aplace, atake\n","ARCH":"\nARCH\n Operating system CPU architecture for which the JavaScript runtime binary\n was compiled.\n\n Current possible values:\n\n - arm\n - arm64\n - ia32\n - mips\n - mipsel\n - ppc\n - ppc64\n - s390\n - s390x\n - x32\n - x64\n\n Examples\n --------\n > ARCH\n \n\n See Also\n --------\n PLATFORM\n","argumentFunction":"\nargumentFunction( idx )\n Returns a function which always returns a specified argument.\n\n The input argument corresponds to the zero-based index of the argument to\n return.\n\n Parameters\n ----------\n idx: integer\n Argument index to return (zero-based).\n\n Returns\n -------\n out: Function\n Argument function.\n\n Examples\n --------\n > var argn = argumentFunction( 1 );\n > var v = argn( 3.14, -3.14, 0.0 )\n -3.14\n > v = argn( -1.0, -0.0, 1.0 )\n -0.0\n > v = argn( 'beep', 'boop', 'bop' )\n 'boop'\n > v = argn( 'beep' )\n undefined\n\n See Also\n --------\n constantFunction, identity\n","ARGV":"\nARGV\n An array containing command-line arguments passed when launching the calling\n process.\n\n The first element is the absolute pathname of the executable that started\n the calling process.\n\n The second element is the path of the executed file.\n\n Any additional elements are additional command-line arguments.\n\n In browser environments, the array is empty.\n\n Examples\n --------\n > var execPath = ARGV[ 0 ]\n e.g., /usr/local/bin/node\n\n See Also\n --------\n ENV\n","array":"\narray( [buffer,] [options] )\n Returns a multidimensional array.\n\n Parameters\n ----------\n buffer: Array|TypedArray|Buffer|ndarray (optional)\n Data source.\n\n options: Object (optional)\n Options.\n\n options.buffer: Array|TypedArray|Buffer|ndarray (optional)\n Data source. If provided along with a `buffer` argument, the argument\n takes precedence.\n\n options.dtype: string (optional)\n Underlying storage data type. If not specified and a data source is\n provided, the data type is inferred from the provided data source. If an\n input data source is not of the same type, this option specifies the\n data type to which to cast the input data. For non-ndarray generic array\n data sources, the function casts generic array data elements to the\n default data type. In order to prevent this cast, the `dtype` option\n must be explicitly set to `'generic'`. Any time a cast is required, the\n `copy` option is set to `true`, as memory must be copied from the data\n source to an output data buffer. Default: 'float64'.\n\n options.order: string (optional)\n Specifies the memory layout of the data source as either row-major (C-\n style) or column-major (Fortran-style). The option may be one of the\n following values:\n\n - 'row-major': the order of the returned array is row-major.\n - 'column-major': the order of the returned array is column-major.\n - 'any': if a data source is column-major and not row-major, the order\n of the returned array is column-major; otherwise, the order of the\n returned array is row-major.\n - 'same': the order of the returned array matches the order of an input\n data source.\n\n Note that specifying an order which differs from the order of a\n provided data source does *not* entail a conversion from one memory\n layout to another. In short, this option is descriptive, not\n prescriptive. Default: 'row-major'.\n\n options.shape: Array (optional)\n Array shape (dimensions). If a shape is not specified, the function\n attempts to infer a shape based on a provided data source. For example,\n if provided a nested array, the function resolves nested array\n dimensions. If provided a multidimensional array data source, the\n function uses the array's associated shape. For most use cases, such\n inference suffices. For the remaining use cases, specifying a shape is\n necessary. For example, provide a shape to create a multidimensional\n array view over a linear data buffer, ignoring any existing shape meta\n data associated with a provided data source.\n\n options.flatten: boolean (optional)\n Boolean indicating whether to automatically flatten generic array data\n sources. If an array shape is not specified, the shape is inferred from\n the dimensions of nested arrays prior to flattening. If a use case\n requires partial flattening, partially flatten prior to invoking this\n function and set the option value to `false` to prevent further\n flattening during invocation. Default: true.\n\n options.copy: boolean (optional)\n Boolean indicating whether to (shallow) copy source data to a new data\n buffer. The function does *not* perform a deep copy. To prevent\n undesired shared changes in state for generic arrays containing objects,\n perform a deep copy prior to invoking this function. Default: false.\n\n options.ndmin: integer (optional)\n Specifies the minimum number of dimensions. If an array shape has fewer\n dimensions than required by `ndmin`, the function prepends singleton\n dimensions to the array shape in order to satisfy the dimensions\n requirement. Default: 0.\n\n options.casting: string (optional)\n Specifies the casting rule used to determine acceptable casts. The\n option may be one of the following values:\n\n - 'none': only allow casting between identical types.\n - 'equiv': allow casting between identical and byte swapped types.\n - 'safe': only allow \"safe\" casts.\n - 'mostly-safe': allow \"safe casts\" and, for floating-point data types,\n downcasts.\n - 'same-kind': allow \"safe\" casts and casts within the same kind (e.g.,\n between signed integers or between floats).\n - 'unsafe': allow casting between all types (including between integers\n and floats).\n\n Default: 'safe'.\n\n options.codegen: boolean (optional)\n Boolean indicating whether to use code generation. Code generation can\n boost performance, but may be problematic in browser contexts enforcing\n a strict content security policy (CSP). Default: true.\n\n options.mode: string (optional)\n Specifies how to handle indices which exceed array dimensions. The\n option may be one of the following values:\n\n - 'throw': an ndarray instance throws an error when an index exceeds\n array dimensions.\n - 'normalize': an ndarray instance normalizes negative indices and\n throws an error when an index exceeds array dimensions.\n - 'wrap': an ndarray instance wraps around indices exceeding array\n dimensions using modulo arithmetic.\n - 'clamp', an ndarray instance sets an index exceeding array dimensions\n to either `0` (minimum index) or the maximum index.\n\n Default: 'throw'.\n\n options.submode: Array (optional)\n Specifies how to handle subscripts which exceed array dimensions. If a\n mode for a corresponding dimension is equal to\n\n - 'throw': an ndarray instance throws an error when a subscript exceeds\n array dimensions.\n - 'normalize': an ndarray instance normalizes negative subscripts and\n throws an error when a subscript exceeds array dimensions.\n - 'wrap': an ndarray instance wraps around subscripts exceeding array\n dimensions using modulo arithmetic.\n - 'clamp': an ndarray instance sets a subscript exceeding array\n dimensions to either `0` (minimum index) or the maximum index.\n\n If the number of modes is fewer than the number of dimensions, the\n function recycles modes using modulo arithmetic.\n\n Default: [ options.mode ].\n\n options.readonly: boolean (optional)\n Boolean indicating whether an array should be read-only. Default: false.\n\n Returns\n -------\n out: ndarray\n Multidimensional array.\n\n Examples\n --------\n // Create a 2x2 matrix:\n > var arr = array( [ [ 1.0, 2.0 ], [ 3.0, 4.0 ] ] )\n \n\n // Get an element using subscripts:\n > var v = arr.get( 1, 1 )\n 4.0\n\n // Get an element using a linear index:\n > v = arr.iget( 3 )\n 4.0\n\n // Set an element using subscripts:\n > arr.set( 1, 1, 40.0 );\n > arr.get( 1, 1 )\n 40.0\n\n // Set an element using a linear index:\n > arr.iset( 3, 99.0 );\n > arr.get( 1, 1 )\n 99.0\n\n See Also\n --------\n ndarray\n","array2buffer":"\narray2buffer( arr )\n Allocates a buffer using an octet array.\n\n Parameters\n ----------\n arr: Array\n Array (or array-like object) of octets from which to copy.\n\n Returns\n -------\n out: Buffer\n Buffer instance.\n\n Examples\n --------\n > var buf = array2buffer( [ 1, 2, 3, 4 ] )\n [ 1, 2, 3, 4 ]\n\n See Also\n --------\n Buffer, arraybuffer2buffer, copyBuffer, string2buffer\n","array2fancy":"\narray2fancy( x[, options] )\n Converts an array to an object supporting fancy indexing.\n\n An array supporting fancy indexing is an array which supports slicing via\n indexing expressions for both retrieval and assignment.\n\n A fancy array shares the *same* data as the provided input array. Hence, any\n mutations to the returned array will affect the underlying input array and\n vice versa.\n\n For operations returning a new array (e.g., when slicing or invoking an\n instance method), a fancy array returns a new fancy array having the same\n configuration as specified by provided options.\n\n A fancy array supports indexing using positive and negative integers (both\n numeric literals and strings), Slice instances, subsequence expressions,\n mask arrays, boolean arrays, and integer arrays.\n\n A fancy array supports all properties and methods of the input array, and,\n thus, a fancy array can be consumed by any API which supports array-like\n objects.\n\n Indexing expressions provide a convenient and powerful means for creating\n and operating on array views; however, their use does entail a performance\n cost. Indexing expressions are best suited for interactive use (e.g., in the\n REPL) and scripting. For performance critical applications, prefer\n equivalent functional APIs supporting array-like objects.\n\n Fancy arrays support broadcasting in which assigned scalars and single-\n element arrays are repeated (without additional memory allocation) to match\n the length of a target array instance.\n\n Fancy array broadcasting follows the same rules as for ndarrays.\n\n Consequently, when assigning arrays to slices, the array on the right-hand-\n side must be broadcast-compatible with number of elements in the slice.\n\n Fancy arrays support (mostly) safe casts (i.e., any cast which can be\n performed without overflow or loss of precision, with the exception of\n floating-point arrays which are also allowed to downcast from higher\n precision to lower precision).\n\n When attempting to perform an unsafe cast, fancy arrays will raise an\n exception.\n\n When assigning a real-valued scalar to a complex number array (e.g.,\n Complex128Array or Complex64Array), a fancy array will cast the real-valued\n scalar to a complex number argument having an imaginary component equal to\n zero.\n\n In older JavaScript environments which do not support Proxy objects, the use\n of indexing expressions is not supported.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n options: Object (optional)\n Function options.\n\n options.strict: boolean (optional)\n Boolean indicating whether to enforce strict bounds checking. Default:\n false.\n\n options.cache: Object (optional)\n Cache for resolving array index objects. Must have a 'get' method which\n accepts a single argument: a string identifier associated with an array\n index. If an array index associated with a provided identifier exists,\n the 'get' method should return an object having the following\n properties:\n\n - data: the underlying index array.\n - type: the index type. Must be either 'mask', 'bool', or 'int'.\n - dtype: the data type of the underlying array.\n\n If an array index is not associated with a provided identifier, the\n 'get' method should return `null`.\n\n Default: `ArrayIndex`.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Output array supporting fancy indexing.\n\n Examples\n --------\n > var y = array2fancy( [ 1, 2, 3, 4 ] );\n > y[ '1::2' ]\n [ 2, 4 ]\n > y[ '::-1' ]\n [ 4, 3, 2, 1 ]\n\n\narray2fancy.factory( [options] )\n Returns a function for converting an array to an object supporting fancy\n indexing.\n\n Parameters\n ----------\n options: Object (optional)\n Function options.\n\n options.strict: boolean (optional)\n Boolean indicating whether to enforce strict bounds checking by default.\n Default: false.\n\n options.cache: Object (optional)\n Cache for resolving array index objects. Must have a 'get' method which\n accepts a single argument: a string identifier associated with an array\n index. If an array index associated with a provided identifier exists,\n the 'get' method should return an object having the following\n properties:\n\n - data: the underlying index array.\n - type: the index type. Must be either 'mask', 'bool', or 'int'.\n - dtype: the data type of the underlying array.\n\n If an array index is not associated with a provided identifier, the\n 'get' method should return `null`.\n\n Default: `ArrayIndex`.\n\n Returns\n -------\n fcn: Function\n Function for converting an array to an object supporting fancy indexing.\n\n Examples\n --------\n > var f = array2fancy.factory();\n > var y = f( [ 1, 2, 3, 4 ] );\n > y[ '1::2' ]\n [ 2, 4 ]\n > y[ '::-1' ]\n [ 4, 3, 2, 1 ]\n\n\narray2fancy.idx( x[, options] )\n Wraps a provided array as an array index object.\n\n For documentation and usage, see `ArrayIndex`.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n options: Object (optional)\n Function options.\n\n options.persist: boolean (optional)\n Boolean indicating whether to continue persisting an index object after\n first usage. Default: false.\n\n Returns\n -------\n out: ArrayIndex\n ArrayIndex instance.\n\n Examples\n --------\n > var idx = array2fancy.idx( [ 1, 2, 3, 4 ] );\n\n See Also\n --------\n aslice, FancyArray\n","array2fancy.factory":"\narray2fancy.factory( [options] )\n Returns a function for converting an array to an object supporting fancy\n indexing.\n\n Parameters\n ----------\n options: Object (optional)\n Function options.\n\n options.strict: boolean (optional)\n Boolean indicating whether to enforce strict bounds checking by default.\n Default: false.\n\n options.cache: Object (optional)\n Cache for resolving array index objects. Must have a 'get' method which\n accepts a single argument: a string identifier associated with an array\n index. If an array index associated with a provided identifier exists,\n the 'get' method should return an object having the following\n properties:\n\n - data: the underlying index array.\n - type: the index type. Must be either 'mask', 'bool', or 'int'.\n - dtype: the data type of the underlying array.\n\n If an array index is not associated with a provided identifier, the\n 'get' method should return `null`.\n\n Default: `ArrayIndex`.\n\n Returns\n -------\n fcn: Function\n Function for converting an array to an object supporting fancy indexing.\n\n Examples\n --------\n > var f = array2fancy.factory();\n > var y = f( [ 1, 2, 3, 4 ] );\n > y[ '1::2' ]\n [ 2, 4 ]\n > y[ '::-1' ]\n [ 4, 3, 2, 1 ]","array2fancy.idx":"\narray2fancy.idx( x[, options] )\n Wraps a provided array as an array index object.\n\n For documentation and usage, see `ArrayIndex`.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n options: Object (optional)\n Function options.\n\n options.persist: boolean (optional)\n Boolean indicating whether to continue persisting an index object after\n first usage. Default: false.\n\n Returns\n -------\n out: ArrayIndex\n ArrayIndex instance.\n\n Examples\n --------\n > var idx = array2fancy.idx( [ 1, 2, 3, 4 ] );\n\n See Also\n --------\n aslice, FancyArray","array2iterator":"\narray2iterator( src[, mapFcn[, thisArg]] )\n Returns an iterator which iterates over the elements of an array-like\n object.\n\n When invoked, an input function is provided three arguments:\n\n - value: iterated value.\n - index: iterated value index.\n - src: source array-like object.\n\n If an environment supports Symbol.iterator, the returned iterator is\n iterable.\n\n If an environment supports Symbol.iterator, the function explicitly does not\n invoke an array's `@@iterator` method, regardless of whether this method is\n defined. To convert an array to an implementation defined iterator, invoke\n this method directly.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Array-like object from which to create the iterator.\n\n mapFcn: Function (optional)\n Function to invoke for each iterated value.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n iterator: Object\n Iterator.\n\n iterator.next(): Function\n Returns an iterator protocol-compliant object containing the next\n iterated value (if one exists) and a boolean flag indicating whether the\n iterator is finished.\n\n iterator.return( [value] ): Function\n Finishes an iterator and returns a provided value.\n\n Examples\n --------\n > var it = array2iterator( [ 1, 2, 3, 4 ] );\n > var v = it.next().value\n 1\n > v = it.next().value\n 2\n\n See Also\n --------\n iterator2array, circarray2iterator, array2iteratorRight, stridedarray2iterator\n","array2iteratorRight":"\narray2iteratorRight( src[, mapFcn[, thisArg]] )\n Returns an iterator which iterates from right to left over the elements of\n an array-like object.\n\n When invoked, an input function is provided three arguments:\n\n - value: iterated value.\n - index: iterated value index.\n - src: source array-like object.\n\n If an environment supports Symbol.iterator, the returned iterator is\n iterable.\n\n If an environment supports Symbol.iterator, the function explicitly does not\n invoke an array's `@@iterator` method, regardless of whether this method is\n defined. To convert an array to an implementation defined iterator, invoke\n this method directly.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Array-like object from which to create the iterator.\n\n mapFcn: Function (optional)\n Function to invoke for each iterated value.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n iterator: Object\n Iterator.\n\n iterator.next(): Function\n Returns an iterator protocol-compliant object containing the next\n iterated value (if one exists) and a boolean flag indicating whether the\n iterator is finished.\n\n iterator.return( [value] ): Function\n Finishes an iterator and returns a provided value.\n\n Examples\n --------\n > var it = array2iteratorRight( [ 1, 2, 3, 4 ] );\n > var v = it.next().value\n 4\n > v = it.next().value\n 3\n\n See Also\n --------\n iterator2array, array2iterator\n","ArrayBuffer":"\nArrayBuffer( size )\n Returns an array buffer having a specified number of bytes.\n\n Buffer contents are initialized to 0.\n\n Parameters\n ----------\n size: integer\n Number of bytes.\n\n Returns\n -------\n out: ArrayBuffer\n An array buffer.\n\n Examples\n --------\n > var buf = new ArrayBuffer( 5 )\n \n\n\nArrayBuffer.length\n Number of input arguments the constructor accepts.\n\n Examples\n --------\n > ArrayBuffer.length\n 1\n\n\nArrayBuffer.isView( arr )\n Returns a boolean indicating if provided an array buffer view.\n\n Parameters\n ----------\n arr: any\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating if an input argument is a buffer view.\n\n Examples\n --------\n > var arr = new Float64Array( 10 );\n > ArrayBuffer.isView( arr )\n true\n\n\nArrayBuffer.prototype.byteLength\n Read-only property which returns the length (in bytes) of the array buffer.\n\n Examples\n --------\n > var buf = new ArrayBuffer( 5 );\n > buf.byteLength\n 5\n\n\nArrayBuffer.prototype.slice( [start[, end]] )\n Copies the bytes of an array buffer to a new array buffer.\n\n Parameters\n ----------\n start: integer (optional)\n Index at which to start copying buffer contents (inclusive). If\n negative, the index is relative to the end of the buffer.\n\n end: integer (optional)\n Index at which to stop copying buffer contents (exclusive). If negative,\n the index is relative to the end of the buffer.\n\n Returns\n -------\n out: ArrayBuffer\n A new array buffer whose contents have been copied from the calling\n array buffer.\n\n Examples\n --------\n > var b1 = new ArrayBuffer( 10 );\n > var b2 = b1.slice( 2, 6 );\n > var bool = ( b1 === b2 )\n false\n > b2.byteLength\n 4\n\n See Also\n --------\n Buffer, Float32Array, Float64Array, Int16Array, Int32Array, Int8Array, SharedArrayBuffer, Uint16Array, Uint32Array, Uint8Array, Uint8ClampedArray\n","ArrayBuffer.length":"\nArrayBuffer.length\n Number of input arguments the constructor accepts.\n\n Examples\n --------\n > ArrayBuffer.length\n 1","ArrayBuffer.isView":"\nArrayBuffer.isView( arr )\n Returns a boolean indicating if provided an array buffer view.\n\n Parameters\n ----------\n arr: any\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating if an input argument is a buffer view.\n\n Examples\n --------\n > var arr = new Float64Array( 10 );\n > ArrayBuffer.isView( arr )\n true","ArrayBuffer.prototype.byteLength":"\nArrayBuffer.prototype.byteLength\n Read-only property which returns the length (in bytes) of the array buffer.\n\n Examples\n --------\n > var buf = new ArrayBuffer( 5 );\n > buf.byteLength\n 5","ArrayBuffer.prototype.slice":"\nArrayBuffer.prototype.slice( [start[, end]] )\n Copies the bytes of an array buffer to a new array buffer.\n\n Parameters\n ----------\n start: integer (optional)\n Index at which to start copying buffer contents (inclusive). If\n negative, the index is relative to the end of the buffer.\n\n end: integer (optional)\n Index at which to stop copying buffer contents (exclusive). If negative,\n the index is relative to the end of the buffer.\n\n Returns\n -------\n out: ArrayBuffer\n A new array buffer whose contents have been copied from the calling\n array buffer.\n\n Examples\n --------\n > var b1 = new ArrayBuffer( 10 );\n > var b2 = b1.slice( 2, 6 );\n > var bool = ( b1 === b2 )\n false\n > b2.byteLength\n 4\n\n See Also\n --------\n Buffer, Float32Array, Float64Array, Int16Array, Int32Array, Int8Array, SharedArrayBuffer, Uint16Array, Uint32Array, Uint8Array, Uint8ClampedArray","arraybuffer2buffer":"\narraybuffer2buffer( buf[, byteOffset[, length]] )\n Allocates a buffer from an ArrayBuffer.\n\n The behavior of this function varies across Node.js versions due to changes\n in the underlying Node.js APIs:\n\n - <3.0.0: the function copies ArrayBuffer bytes to a new Buffer instance.\n - >=3.0.0 and <5.10.0: if provided a byte offset, the function copies\n ArrayBuffer bytes to a new Buffer instance; otherwise, the function\n returns a view of an ArrayBuffer without copying the underlying memory.\n - <6.0.0: if provided an empty ArrayBuffer, the function returns an empty\n Buffer which is not an ArrayBuffer view.\n - >=6.0.0: the function returns a view of an ArrayBuffer without copying\n the underlying memory.\n\n Parameters\n ----------\n buf: ArrayBuffer\n Input array buffer.\n\n byteOffset: integer (optional)\n Index offset specifying the location of the first byte.\n\n length: integer (optional)\n Number of bytes to expose from the underlying ArrayBuffer.\n\n Returns\n -------\n out: Buffer\n Buffer instance.\n\n Examples\n --------\n > var ab = new ArrayBuffer( 10 )\n \n > var buf = arraybuffer2buffer( ab )\n \n > var len = buf.length\n 10\n > buf = arraybuffer2buffer( ab, 2, 6 )\n \n > len = buf.length\n 6\n\n See Also\n --------\n Buffer, array2buffer, copyBuffer, string2buffer\n","arrayCtors":"\narrayCtors( dtype )\n Returns an array constructor.\n\n The function returns constructors for the following data types:\n\n - float32: single-precision floating-point numbers.\n - float64: double-precision floating-point numbers.\n - complex64: single-precision complex floating-point numbers.\n - complex128: double-precision complex floating-point numbers.\n - bool: boolean values.\n - generic: values of any type.\n - int16: signed 16-bit integers.\n - int32: signed 32-bit integers.\n - int8: signed 8-bit integers.\n - uint16: unsigned 16-bit integers.\n - uint32: unsigned 32-bit integers.\n - uint8: unsigned 8-bit integers.\n - uint8c: unsigned clamped 8-bit integers.\n\n Parameters\n ----------\n dtype: string\n Data type.\n\n Returns\n -------\n out: Function|null\n Constructor.\n\n Examples\n --------\n > var ctor = arrayCtors( 'float64' )\n \n > ctor = arrayCtors( 'float' )\n null\n\n See Also\n --------\n typedarrayCtors\n","arrayDataType":"\narrayDataType( array )\n Returns the data type of an array.\n\n If provided an argument having an unknown or unsupported type, the function\n returns `null`.\n\n Parameters\n ----------\n array: any\n Input value.\n\n Returns\n -------\n out: string|null\n Data type.\n\n Examples\n --------\n > var arr = new Float64Array( 10 );\n > var dt = arrayDataType( arr )\n 'float64'\n > dt = arrayDataType( 'beep' )\n null\n\n See Also\n --------\n arrayDataTypes\n","arrayDataTypes":"\narrayDataTypes( [kind] )\n Returns a list of array data types.\n\n When not provided a data type \"kind\", the function returns an array\n containing the following data types:\n\n - float32: single-precision floating-point numbers.\n - float64: double-precision floating-point numbers.\n - complex64: single-precision complex floating-point numbers.\n - complex128: double-precision complex floating-point numbers.\n - bool: boolean values.\n - generic: values of any type.\n - int16: signed 16-bit integers.\n - int32: signed 32-bit integers.\n - int8: signed 8-bit integers.\n - uint16: unsigned 16-bit integers.\n - uint32: unsigned 32-bit integers.\n - uint8: unsigned 8-bit integers.\n - uint8c: unsigned clamped 8-bit integers.\n\n The function supports the following data type \"kinds\":\n\n - floating_point: floating-point data types.\n - real_floating_point: real-valued floating-point data types.\n - complex_floating_point: complex-valued floating-point data types.\n - boolean: boolean data types.\n - integer: integer data types.\n - signed_integer: signed integer data types.\n - unsigned_integer: unsigned integer data types.\n - real: real-valued data types.\n - numeric: numeric data types.\n - typed: typed data types.\n - integer_index: integer index data types.\n - boolean_index: boolean index data types.\n - mask_index: mask index data types.\n - typed_index: typed index data types.\n - index: index data types.\n - all: all data types.\n\n Additionally, the function supports extending the \"kinds\" listed above by\n appending a '_and_generic' suffix to the kind name (e.g., real_and_generic).\n\n Parameters\n ----------\n kind: string (optional)\n Data type kind.\n\n Returns\n -------\n out: Array\n List of array data types.\n\n Examples\n --------\n > var out = arrayDataTypes()\n [...]\n > out = arrayDataTypes( 'floating_point' )\n [...]\n > out = arrayDataTypes( 'floating_point_and_generic' )\n [...]\n\n See Also\n --------\n typedarrayDataTypes, ndarrayDataTypes\n","ArrayIndex":"\nArrayIndex( x[, options] )\n Wraps a provided array as an array index object.\n\n Array index instances have no explicit functionality; however, they are used\n by \"fancy\" arrays for element retrieval and assignment.\n\n By default, an instance is invalidated and removed from an internal cache\n immediately after a consumer resolves the underlying data associated with an\n instance using the `get` static method. Immediate invalidation and cache\n removal ensures that references to the underlying array are not the source\n of memory leaks.\n\n Because instances leverage an internal cache implementing the Singleton\n pattern, one must be sure to use the same constructor as consumers. If one\n uses a different constructor, the consumer will *not* be able to resolve the\n original wrapped array, as the consumer will attempt to resolve an instance\n in the wrong internal cache.\n\n Because non-persisted instances are freed after first use, in order to avoid\n holding onto memory and to allow garbage collection, one should avoid\n scenarios in which an instance is never used.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n options: Object (optional)\n Function options.\n\n options.persist: boolean (optional)\n Boolean indicating whether to continue persisting an index object after\n first usage. Default: false.\n\n Returns\n -------\n out: ArrayIndex\n ArrayIndex instance.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n\n\nArrayIndex.free( id )\n Frees the instance associated with a provided identifier.\n\n Parameters\n ----------\n id: string\n Instance identifier.\n\n Returns\n -------\n out: boolean\n Boolean indicating whether an instance was successfully freed.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > ArrayIndex.free( idx.id )\n \n\n\nArrayIndex.get( id )\n Returns the array associated with the instance having a provided identifier.\n\n Parameters\n ----------\n id: string\n Instance identifier.\n\n Returns\n -------\n out: Object\n Object containing array data.\n\n out.data: Array|TypedArray|Object\n The underlying array associated with the provided identifier.\n\n out.type: string\n The type of array index.\n\n out.dtype: string\n The data type of the underlying array.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > ArrayIndex.get( idx.id )\n {...}\n\n\nArrayIndex.prototype.data\n Read-only property returning the underlying index array.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Array data.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.data\n [ 1, 2, 3, 4 ]\n\n\nArrayIndex.prototype.dtype\n Read-only property returning the underlying data type of the index array.\n\n Returns\n -------\n out: string\n Array data type.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.dtype\n 'generic'\n\n\nArrayIndex.prototype.id\n Read-only property returning the unique identifier associated with an\n instance.\n\n Returns\n -------\n out: string\n String identifier.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.id\n \n\n\nArrayIndex.prototype.isCached\n Read-only property returning a boolean indicating whether an array index is\n actively cached.\n\n Returns\n -------\n out: boolean\n Boolean indicating whether an array index is actively cached.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.isCached\n true\n\n\nArrayIndex.prototype.type\n Read-only property returning the array index type.\n\n Returns\n -------\n out: string\n Array index type.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.type\n \n\n\nArrayIndex.prototype.toString()\n Serializes an instance as a string.\n\n Returns\n -------\n str: string\n Serialized string.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.toString()\n \n\n\nArrayIndex.prototype.toJSON()\n Serializes an instance as a JSON object.\n\n Returns\n -------\n obj: Object\n JSON object.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.toJSON()\n { 'type': 'ArrayIndex', 'data': [ 1, 2, 3, 4 ] }\n\n See Also\n --------\n array2fancy\n","ArrayIndex.free":"\nArrayIndex.free( id )\n Frees the instance associated with a provided identifier.\n\n Parameters\n ----------\n id: string\n Instance identifier.\n\n Returns\n -------\n out: boolean\n Boolean indicating whether an instance was successfully freed.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > ArrayIndex.free( idx.id )\n ","ArrayIndex.get":"\nArrayIndex.get( id )\n Returns the array associated with the instance having a provided identifier.\n\n Parameters\n ----------\n id: string\n Instance identifier.\n\n Returns\n -------\n out: Object\n Object containing array data.\n\n out.data: Array|TypedArray|Object\n The underlying array associated with the provided identifier.\n\n out.type: string\n The type of array index.\n\n out.dtype: string\n The data type of the underlying array.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > ArrayIndex.get( idx.id )\n {...}","ArrayIndex.prototype.data":"\nArrayIndex.prototype.data\n Read-only property returning the underlying index array.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Array data.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.data\n [ 1, 2, 3, 4 ]","ArrayIndex.prototype.dtype":"\nArrayIndex.prototype.dtype\n Read-only property returning the underlying data type of the index array.\n\n Returns\n -------\n out: string\n Array data type.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.dtype\n 'generic'","ArrayIndex.prototype.id":"\nArrayIndex.prototype.id\n Read-only property returning the unique identifier associated with an\n instance.\n\n Returns\n -------\n out: string\n String identifier.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.id\n ","ArrayIndex.prototype.isCached":"\nArrayIndex.prototype.isCached\n Read-only property returning a boolean indicating whether an array index is\n actively cached.\n\n Returns\n -------\n out: boolean\n Boolean indicating whether an array index is actively cached.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.isCached\n true","ArrayIndex.prototype.type":"\nArrayIndex.prototype.type\n Read-only property returning the array index type.\n\n Returns\n -------\n out: string\n Array index type.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.type\n ","ArrayIndex.prototype.toString":"\nArrayIndex.prototype.toString()\n Serializes an instance as a string.\n\n Returns\n -------\n str: string\n Serialized string.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.toString()\n ","ArrayIndex.prototype.toJSON":"\nArrayIndex.prototype.toJSON()\n Serializes an instance as a JSON object.\n\n Returns\n -------\n obj: Object\n JSON object.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.toJSON()\n { 'type': 'ArrayIndex', 'data': [ 1, 2, 3, 4 ] }\n\n See Also\n --------\n array2fancy","arrayMinDataType":"\narrayMinDataType( value )\n Returns the minimum array data type of the closest \"kind\" necessary for\n storing a provided scalar value.\n\n The function does *not* provide precision guarantees for non-integer-valued\n numbers. In other words, the function returns the smallest possible\n floating-point (i.e., inexact) data type for storing numbers having\n decimals.\n\n Parameters\n ----------\n value: any\n Scalar value.\n\n Returns\n -------\n dt: string\n Array data type.\n\n Examples\n --------\n > var dt = arrayMinDataType( 3.141592653589793 )\n 'float32'\n > dt = arrayMinDataType( 3 )\n 'uint8'\n > dt = arrayMinDataType( -3 )\n 'int8'\n > dt = arrayMinDataType( '-3' )\n 'generic'\n\n See Also\n --------\n arrayDataTypes, arrayPromotionRules, arraySafeCasts\n","arrayMostlySafeCasts":"\narrayMostlySafeCasts( [dtype] )\n Returns a list of array data types to which a provided array data type can\n be safely cast and, for floating-point data types, can be downcast.\n\n If not provided an array data type, the function returns a casting table.\n\n If provided an unrecognized array data type, the function returns `null`.\n\n Parameters\n ----------\n dtype: any (optional)\n Array data type value.\n\n Returns\n -------\n out: Object|Array|null\n Array data types to which a data type can be cast.\n\n Examples\n --------\n > var out = arrayMostlySafeCasts( 'float32' )\n \n\n See Also\n --------\n convertArray, convertArraySame, arrayDataTypes, arraySafeCasts, arraySameKindCasts, ndarrayMostlySafeCasts\n","arrayNextDataType":"\narrayNextDataType( [dtype] )\n Returns the next larger array data type of the same kind.\n\n If not provided a data type, the function returns a table.\n\n If a data type does not have a next larger data type or the next larger type\n is not supported, the function returns `-1`.\n\n If provided an unrecognized data type, the function returns `null`.\n\n Parameters\n ----------\n dtype: string (optional)\n Array data type.\n\n Returns\n -------\n out: Object|string|integer|null\n Next larger type(s).\n\n Examples\n --------\n > var out = arrayNextDataType( 'float32' )\n 'float64'\n\n See Also\n --------\n arrayDataType, arrayDataTypes\n","arrayPromotionRules":"\narrayPromotionRules( [dtype1, dtype2] )\n Returns the array data type with the smallest size and closest \"kind\" to\n which array data types can be safely cast.\n\n If not provided data types, the function returns a type promotion table.\n\n If a data type to which data types can be safely cast does *not* exist (or\n is not supported), the function returns `-1`.\n\n If provided an unrecognized data type, the function returns `null`.\n\n Parameters\n ----------\n dtype1: any (optional)\n Array data type.\n\n dtype2: any (optional)\n Array data type.\n\n Returns\n -------\n out: Object|string|integer|null\n Promotion rule(s).\n\n Examples\n --------\n > var out = arrayPromotionRules( 'float32', 'int32' )\n 'float64'\n\n See Also\n --------\n arrayDataTypes, arraySafeCasts, ndarrayPromotionRules\n","arraySafeCasts":"\narraySafeCasts( [dtype] )\n Returns a list of array data types to which a provided array data type can\n be safely cast.\n\n If not provided an array data type, the function returns a casting table.\n\n If provided an unrecognized array data type, the function returns `null`.\n\n Parameters\n ----------\n dtype: any (optional)\n Array data type.\n\n Returns\n -------\n out: Object|Array|null\n Array data types to which a data type can be safely cast.\n\n Examples\n --------\n > var out = arraySafeCasts( 'float32' )\n \n\n See Also\n --------\n convertArray, convertArraySame, arrayDataTypes, arrayMostlySafeCasts, arraySameKindCasts, ndarraySafeCasts\n","arraySameKindCasts":"\narraySameKindCasts( [dtype] )\n Returns a list of array data types to which a provided array data type can\n be safely cast or cast within the same \"kind\".\n\n If not provided an array data type, the function returns a casting table.\n\n If provided an unrecognized array data type, the function returns `null`.\n\n Parameters\n ----------\n dtype: any (optional)\n Array data type.\n\n Returns\n -------\n out: Object|Array|null\n Array data types to which a data type can be safely cast or cast within\n the same \"kind\".\n\n Examples\n --------\n > var out = arraySameKindCasts( 'float32' )\n \n\n See Also\n --------\n convertArray, convertArraySame, arrayDataTypes, arraySafeCasts, ndarraySameKindCasts\n","arrayShape":"\narrayShape( arr )\n Determines array dimensions.\n\n Parameters\n ----------\n arr: ArrayLikeObject\n Input array.\n\n Returns\n -------\n out: Array\n Array shape.\n\n Examples\n --------\n > var out = arrayShape( [ [ 1, 2, 3 ], [ 4, 5, 6 ] ] )\n [ 2, 3 ]\n\n See Also\n --------\n ndarray\n","arrayStream":"\narrayStream( src[, options] )\n Creates a readable stream from an array-like object.\n\n In object mode, `null` is a reserved value. If an array contains `null`\n values (e.g., as a means to encode missing values), the stream will\n prematurely end. Consider an alternative encoding or filter `null` values\n prior to invocation.\n\n In binary mode, if an array contains `undefined` values, the stream will\n emit an error. Consider providing a custom serialization function or\n filtering `undefined` values prior to invocation.\n\n If a serialization function fails to return a string or Buffer, the stream\n emits an error.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Source value.\n\n options: Object (optional)\n Options.\n\n options.objectMode: boolean (optional)\n Specifies whether a stream should operate in \"objectMode\". Default:\n false.\n\n options.encoding: string|null (optional)\n Specifies how Buffer objects should be decoded to strings. Default:\n null.\n\n options.highWaterMark: integer (optional)\n Specifies the maximum number of bytes to store in an internal buffer\n before pausing the stream.\n\n options.sep: string (optional)\n Separator used to join streamed data. This option is only applicable\n when a stream is not in \"objectMode\". Default: '\\n'.\n\n options.serialize: Function (optional)\n Serialization function. The default behavior is to serialize streamed\n values as JSON strings. This option is only applicable when a stream is\n not in \"objectMode\".\n\n options.dir: integer (optional)\n Iteration direction. If set to `-1`, a stream iterates over elements\n from right-to-left. Default: 1.\n\n Returns\n -------\n stream: ReadableStream\n Readable stream.\n\n Examples\n --------\n > function fcn( chunk ) { console.log( chunk.toString() ); };\n > var s = arrayStream( [ 1, 2, 3 ] );\n > var o = inspectSinkStream( fcn );\n > s.pipe( o );\n\n\narrayStream.factory( [options] )\n Returns a function for creating readable streams from array-like objects.\n\n Parameters\n ----------\n options: Object (optional)\n Options.\n\n options.objectMode: boolean (optional)\n Specifies whether a stream should operate in \"objectMode\". Default:\n false.\n\n options.encoding: string|null (optional)\n Specifies how Buffer objects should be decoded to strings. Default:\n null.\n\n options.highWaterMark: integer (optional)\n Specifies the maximum number of bytes to store in an internal buffer\n before pausing streaming.\n\n options.sep: string (optional)\n Separator used to join streamed data. This option is only applicable\n when a stream is not in \"objectMode\". Default: '\\n'.\n\n options.serialize: Function (optional)\n Serialization function. The default behavior is to serialize streamed\n values as JSON strings. This option is only applicable when a stream is\n not in \"objectMode\".\n\n options.dir: integer (optional)\n Iteration direction. If set to `-1`, a stream iterates over elements\n from right-to-left. Default: 1.\n\n Returns\n -------\n fcn: Function\n Function for creating readable streams.\n\n Examples\n --------\n > var opts = { 'objectMode': true, 'highWaterMark': 64 };\n > var createStream = arrayStream.factory( opts );\n\n\narrayStream.objectMode( src[, options] )\n Returns an \"objectMode\" readable stream from an array-like object.\n\n In object mode, `null` is a reserved value. If an array contains `null`\n values (e.g., as a means to encode missing values), the stream will\n prematurely end. Consider an alternative encoding or filter `null` values\n prior to invocation.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Source value.\n\n options: Object (optional)\n Options.\n\n options.encoding: string|null (optional)\n Specifies how Buffer objects should be decoded to strings. Default:\n null.\n\n options.highWaterMark: integer (optional)\n Specifies the maximum number of objects to store in an internal buffer\n before pausing streaming.\n\n options.dir: integer (optional)\n Iteration direction. If set to `-1`, a stream iterates over elements\n from right-to-left. Default: 1.\n\n Returns\n -------\n stream: ReadableStream\n Readable stream operating in \"objectMode\".\n\n Examples\n --------\n > function fcn( v ) { console.log( v ); };\n > var s = arrayStream.objectMode( [ 1, 2, 3 ] );\n > var o = inspectSinkStream.objectMode( fcn );\n > s.pipe( o );\n\n See Also\n --------\n circularArrayStream, iteratorStream, stridedArrayStream\n","arrayStream.factory":"\narrayStream.factory( [options] )\n Returns a function for creating readable streams from array-like objects.\n\n Parameters\n ----------\n options: Object (optional)\n Options.\n\n options.objectMode: boolean (optional)\n Specifies whether a stream should operate in \"objectMode\". Default:\n false.\n\n options.encoding: string|null (optional)\n Specifies how Buffer objects should be decoded to strings. Default:\n null.\n\n options.highWaterMark: integer (optional)\n Specifies the maximum number of bytes to store in an internal buffer\n before pausing streaming.\n\n options.sep: string (optional)\n Separator used to join streamed data. This option is only applicable\n when a stream is not in \"objectMode\". Default: '\\n'.\n\n options.serialize: Function (optional)\n Serialization function. The default behavior is to serialize streamed\n values as JSON strings. This option is only applicable when a stream is\n not in \"objectMode\".\n\n options.dir: integer (optional)\n Iteration direction. If set to `-1`, a stream iterates over elements\n from right-to-left. Default: 1.\n\n Returns\n -------\n fcn: Function\n Function for creating readable streams.\n\n Examples\n --------\n > var opts = { 'objectMode': true, 'highWaterMark': 64 };\n > var createStream = arrayStream.factory( opts );","arrayStream.objectMode":"\narrayStream.objectMode( src[, options] )\n Returns an \"objectMode\" readable stream from an array-like object.\n\n In object mode, `null` is a reserved value. If an array contains `null`\n values (e.g., as a means to encode missing values), the stream will\n prematurely end. Consider an alternative encoding or filter `null` values\n prior to invocation.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Source value.\n\n options: Object (optional)\n Options.\n\n options.encoding: string|null (optional)\n Specifies how Buffer objects should be decoded to strings. Default:\n null.\n\n options.highWaterMark: integer (optional)\n Specifies the maximum number of objects to store in an internal buffer\n before pausing streaming.\n\n options.dir: integer (optional)\n Iteration direction. If set to `-1`, a stream iterates over elements\n from right-to-left. Default: 1.\n\n Returns\n -------\n stream: ReadableStream\n Readable stream operating in \"objectMode\".\n\n Examples\n --------\n > function fcn( v ) { console.log( v ); };\n > var s = arrayStream.objectMode( [ 1, 2, 3 ] );\n > var o = inspectSinkStream.objectMode( fcn );\n > s.pipe( o );\n\n See Also\n --------\n circularArrayStream, iteratorStream, stridedArrayStream","arrayview2iterator":"\narrayview2iterator( src[, begin[, end]][, mapFcn[, thisArg]] )\n Returns an iterator which iterates over the elements of an array-like object\n view.\n\n When invoked, an input function is provided four arguments:\n\n - value: iterated value.\n - index: iterated value index.\n - n: iteration count (zero-based).\n - src: source array-like object.\n\n If an environment supports Symbol.iterator, the returned iterator is\n iterable.\n\n If an environment supports Symbol.iterator, the function explicitly does not\n invoke an array's `@@iterator` method, regardless of whether this method is\n defined. To convert an array to an implementation defined iterator, invoke\n this method directly.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Array-like object from which to create the iterator.\n\n begin: integer (optional)\n Starting index (inclusive). When negative, determined relative to the\n last element. Default: 0.\n\n end: integer (optional)\n Ending index (non-inclusive). When negative, determined relative to the\n last element. Default: src.length.\n\n mapFcn: Function (optional)\n Function to invoke for each iterated value.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n iterator: Object\n Iterator.\n\n iterator.next(): Function\n Returns an iterator protocol-compliant object containing the next\n iterated value (if one exists) and a boolean flag indicating whether the\n iterator is finished.\n\n iterator.return( [value] ): Function\n Finishes an iterator and returns a provided value.\n\n Examples\n --------\n > var it = arrayview2iterator( [ 1, 2, 3, 4 ], 1, 3 );\n > var v = it.next().value\n 2\n > v = it.next().value\n 3\n\n See Also\n --------\n iterator2array, array2iterator, stridedarray2iterator, arrayview2iteratorRight\n","arrayview2iteratorRight":"\narrayview2iteratorRight( src[, begin[, end]][, mapFcn[, thisArg]] )\n Returns an iterator which iterates from right to left over the elements of\n an array-like object view.\n\n When invoked, an input function is provided four arguments:\n\n - value: iterated value.\n - index: iterated value index.\n - n: iteration count (zero-based).\n - src: source array-like object.\n\n If an environment supports Symbol.iterator, the returned iterator is\n iterable.\n\n If an environment supports Symbol.iterator, the function explicitly does not\n invoke an array's `@@iterator` method, regardless of whether this method is\n defined. To convert an array to an implementation defined iterator, invoke\n this method directly.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Array-like object from which to create the iterator.\n\n begin: integer (optional)\n Starting index (inclusive). When negative, determined relative to the\n last element. Default: 0.\n\n end: integer (optional)\n Ending index (non-inclusive). When negative, determined relative to the\n last element. Default: src.length.\n\n mapFcn: Function (optional)\n Function to invoke for each iterated value.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n iterator: Object\n Iterator.\n\n iterator.next(): Function\n Returns an iterator protocol-compliant object containing the next\n iterated value (if one exists) and a boolean flag indicating whether the\n iterator is finished.\n\n iterator.return( [value] ): Function\n Finishes an iterator and returns a provided value.\n\n Examples\n --------\n > var it = arrayview2iteratorRight( [ 1, 2, 3, 4 ], 1, 3 );\n > var v = it.next().value\n 3\n > v = it.next().value\n 2\n\n See Also\n --------\n iterator2array, array2iteratorRight, stridedarray2iterator, arrayview2iterator\n","aslice":"\naslice( x[, start[, end]] )\n Returns a shallow copy of a portion of an array.\n\n If provided an array-like object having a `slice` method, the function\n defers execution to that method and assumes that the method has the\n following signature:\n\n x.slice( start, end )\n\n If provided an array-like object without a `slice` method, the function\n copies input array elements to a new generic array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n start: integer (optional)\n Starting index (inclusive). Default: 0.\n\n end: integer (optional)\n Ending index (exclusive). Default: x.length.\n\n Returns\n -------\n out: Array|TypedArray\n Output array.\n\n Examples\n --------\n > var out = aslice( [ 1, 2, 3, 4 ] )\n [ 1, 2, 3, 4 ]\n > out = aslice( [ 1, 2, 3, 4 ], 1 )\n [ 2, 3, 4 ]\n > out = aslice( [ 1, 2, 3, 4 ], 1, 3 )\n [ 2, 3 ]\n\n See Also\n --------\n atake\n","AsyncIteratorSymbol":"\nAsyncIteratorSymbol\n Async iterator symbol.\n\n This symbol specifies the default async iterator for an object.\n\n The symbol is only supported in ES2018+ environments. For non-supporting\n environments, the value is `null`.\n\n Examples\n --------\n > var s = AsyncIteratorSymbol\n\n See Also\n --------\n Symbol, IteratorSymbol\n","atake":"\natake( x, indices[, options] )\n Takes elements from an array.\n\n If `indices` is an empty array, the function returns an empty array.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n indices: ArrayLikeObject\n List of element indices.\n\n options: Object (optional)\n Function options.\n\n options.mode: string (optional)\n Specifies how to handle an index outside the interval [0, max], where\n `max` is the maximum possible array index. If equal to 'throw', the\n function throws an error. If equal to 'normalize', the function throws\n an error if provided an out-of-bounds normalized index. If equal to\n 'wrap', the function wraps around an index using modulo arithmetic. If\n equal to 'clamp', the function sets an index to either 0 (minimum index)\n or the maximum index. Default: 'normalize'.\n\n Returns\n -------\n out: Array|TypedArray\n Output array.\n\n Examples\n --------\n > var x = [ 1, 2, 3, 4 ];\n > var y = atake( x, [ 1, 3 ] )\n [ 2, 4 ]\n\n See Also\n --------\n aput, aslice\n","azeros":"\nazeros( length[, dtype] )\n Returns a zero-filled array having a specified length.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754).\n - float32: single-precision floating-point numbers (IEEE 754).\n - complex128: double-precision complex floating-point numbers.\n - complex64: single-precision complex floating-point numbers.\n - int32: 32-bit two's complement signed integers.\n - uint32: 32-bit unsigned integers.\n - int16: 16-bit two's complement signed integers.\n - uint16: 16-bit unsigned integers.\n - int8: 8-bit two's complement signed integers.\n - uint8: 8-bit unsigned integers.\n - uint8c: 8-bit unsigned integers clamped to 0-255.\n - generic: generic JavaScript values.\n\n The default array data type is `float64`.\n\n Parameters\n ----------\n length: integer\n Array length.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = azeros( 2 )\n [ 0.0, 0.0 ]\n > arr = azeros( 2, 'float32' )\n [ 0.0, 0.0 ]\n\n See Also\n --------\n aempty, afull, anans, aones, azerosLike, ndzeros\n","azerosLike":"\nazerosLike( x[, dtype] )\n Returns a zero-filled array having the same length and data type as a\n provided input array.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754).\n - float32: single-precision floating-point numbers (IEEE 754).\n - complex128: double-precision complex floating-point numbers.\n - complex64: single-precision complex floating-point numbers.\n - int32: 32-bit two's complement signed integers.\n - uint32: 32-bit unsigned integers.\n - int16: 16-bit two's complement signed integers.\n - uint16: 16-bit unsigned integers.\n - int8: 8-bit two's complement signed integers.\n - uint8: 8-bit unsigned integers.\n - uint8c: 8-bit unsigned integers clamped to 0-255.\n - generic: generic JavaScript values.\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var x = new Float64Array( 2 );\n > var y = azerosLike( x )\n