Base Statistics

Standard library base statistical functions.

Usage

var stats = require( '@stdlib/stats/base' );

stats

Standard library base statistical functions.

var ns = stats;
// returns {...}

The namespace contains the following sub-namespaces:

• dists: standard library probability distribution modules.

The namespace contains the following statistical functions:

• cumax( N, x, strideX, y, strideY ): calculate the cumulative maximum of a strided array.
• cumaxabs( N, x, strideX, y, strideY ): calculate the cumulative maximum absolute value of a strided array.
• cumin( N, x, strideX, y, strideY ): calculate the cumulative minimum of a strided array.
• cuminabs( N, x, strideX, y, strideY ): calculate the cumulative minimum absolute value of a strided array.
• dcumax( N, x, strideX, y, strideY ): calculate the cumulative maximum of double-precision floating-point strided array elements.
• dcumaxabs( N, x, strideX, y, strideY ): calculate the cumulative maximum absolute value of double-precision floating-point strided array elements.
• dcumin( N, x, strideX, y, strideY ): calculate the cumulative minimum of double-precision floating-point strided array elements.
• dcuminabs( N, x, strideX, y, strideY ): calculate the cumulative minimum absolute value of double-precision floating-point strided array elements.
• dmax( N, x, stride ): calculate the maximum value of a double-precision floating-point strided array.
• dmaxabs( N, x, stride ): calculate the maximum absolute value of a double-precision floating-point strided array.
• dmaxsorted( N, x, stride ): calculate the maximum value of a sorted double-precision floating-point strided array.
• dmean( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array.
• dmeankbn( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using an improved Kahan–Babuška algorithm.
• dmeankbn2( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm.
• dmeanli( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using a one-pass trial mean algorithm.
• dmeanlipw( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using a one-pass trial mean algorithm with pairwise summation.
• dmeanors( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using ordinary recursive summation.
• dmeanpn( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using a two-pass error correction algorithm.
• dmeanpw( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using pairwise summation.
• dmeanwd( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using Welford's algorithm.
• dmediansorted( N, x, stride ): calculate the median value of a sorted double-precision floating-point strided array.
• dmin( N, x, stride ): calculate the minimum value of a double-precision floating-point strided array.
• dminabs( N, x, stride ): calculate the minimum absolute value of a double-precision floating-point strided array.
• dminsorted( N, x, stride ): calculate the minimum value of a sorted double-precision floating-point strided array.
• dnanmax( N, x, stride ): calculate the maximum value of a double-precision floating-point strided array, ignoring NaN values.
• dnanmaxabs( N, x, stride ): calculate the maximum absolute value of a double-precision floating-point strided array, ignoring NaN values.
• dnanmean( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array, ignoring NaN values.
• dnanmeanors( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array, ignoring NaN values and using ordinary recursive summation.
• dnanmeanpn( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array, ignoring NaN values and using a two-pass error correction algorithm.
• dnanmeanwd( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array, using Welford's algorithm and ignoring NaN values.
• dnanmin( N, x, stride ): calculate the minimum value of a double-precision floating-point strided array, ignoring NaN values.
• dnanminabs( N, x, stride ): calculate the minimum absolute value of a double-precision floating-point strided array, ignoring NaN values.
• dnanrange( N, x, stride ): calculate the range of a double-precision floating-point strided array, ignoring NaN values.
• drange( N, x, stride ): calculate the range of a double-precision floating-point strided array.
• dsmean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation and returning an extended precision result.
• dsmeanors( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation with extended accumulation and returning an extended precision result.
• dsmeanpn( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm with extended accumulation and returning an extended precision result.
• dsmeanpw( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using pairwise summation with extended accumulation and returning an extended precision result.
• dsmeanwd( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using Welford's algorithm with extended accumulation and returning an extended precision result.
• dsnanmean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values, using extended accumulation, and returning an extended precision result.
• dsnanmeanors( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.
• dsnanmeanpn( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values, using a two-pass error correction algorithm with extended accumulation, and returning an extended precision result.
• dsnanmeanwd( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values, using Welford's algorithm with extended accumulation, and returning an extended precision result.
• dstdev( N, correction, x, stride ): calculate the standard deviation of a double-precision floating-point strided array.
• dstdevpn( N, correction, x, stride ): calculate the standard deviation of a double-precision floating-point strided array using a two-pass algorithm.
• dstdevtk( N, correction, x, stride ): calculate the standard deviation of a double-precision floating-point strided array using a one-pass textbook algorithm.
• dstdevwd( N, correction, x, stride ): calculate the standard deviation of a double-precision floating-point strided array using Welford's algorithm.
• dstdevyc( N, correction, x, stride ): calculate the standard deviation of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.
• dsvariance( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array using extended accumulation and returning an extended precision result.
• dsvariancepn( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array using a two-pass algorithm with extended accumulation and returning an extended precision result.
• dvariance( N, correction, x, stride ): calculate the variance of a double-precision floating-point strided array.
• dvariancepn( N, correction, x, stride ): calculate the variance of a double-precision floating-point strided array using a two-pass algorithm.
• dvariancetk( N, correction, x, stride ): calculate the variance of a double-precision floating-point strided array using a one-pass textbook algorithm.
• dvariancewd( N, correction, x, stride ): calculate the variance of a double-precision floating-point strided array using Welford's algorithm.
• dvarianceyc( N, correction, x, stride ): calculate the variance of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.
• max( N, x, stride ): calculate the maximum value of a strided array.
• maxabs( N, x, stride ): calculate the maximum absolute value of a strided array.
• maxsorted( N, x, stride ): calculate the maximum value of a sorted strided array.
• mean( N, x, stride ): calculate the arithmetic mean of a strided array.
• meankbn( N, x, stride ): calculate the arithmetic mean of a strided array using an improved Kahan–Babuška algorithm.
• meankbn2( N, x, stride ): calculate the arithmetic mean of a strided array using a second-order iterative Kahan–Babuška algorithm.
• meanors( N, x, stride ): calculate the arithmetic mean of a strided array using ordinary recursive summation.
• meanpn( N, x, stride ): calculate the arithmetic mean of a strided array using a two-pass error correction algorithm.
• meanpw( N, x, stride ): calculate the arithmetic mean of a strided array using pairwise summation.
• meanwd( N, x, stride ): calculate the arithmetic mean of a strided array using Welford's algorithm.
• min( N, x, stride ): calculate the minimum value of a strided array.
• minabs( N, x, stride ): calculate the minimum absolute value of a strided array.
• minsorted( N, x, stride ): calculate the minimum value of a sorted strided array.
• nanmax( N, x, stride ): calculate the maximum value of a strided array, ignoring NaN values.
• nanmaxabs( N, x, stride ): calculate the maximum absolute value of a strided array, ignoring NaN values.
• nanmean( N, x, stride ): calculate the arithmetic mean of a strided array, ignoring NaN values.
• nanmeanors( N, x, stride ): calculate the arithmetic mean of a strided array, ignoring NaN values and using ordinary recursive summation.
• nanmeanpn( N, x, stride ): calculate the arithmetic mean of a strided array, ignoring NaN values and using a two-pass error correction algorithm.
• nanmeanwd( N, x, stride ): calculate the arithmetic mean of a strided array, ignoring NaN values and using Welford's algorithm.
• nanmin( N, x, stride ): calculate the minimum value of a strided array, ignoring NaN values.
• nanminabs( N, x, stride ): calculate the minimum absolute value of a strided array, ignoring NaN values.
• nanrange( N, x, stride ): calculate the range of a strided array, ignoring NaN values.
• range( N, x, stride ): calculate the range of a strided array.
• scumax( N, x, strideX, y, strideY ): calculate the cumulative maximum of single-precision floating-point strided array elements.
• scumaxabs( N, x, strideX, y, strideY ): calculate the cumulative maximum absolute value of single-precision floating-point strided array elements.
• scumin( N, x, strideX, y, strideY ): calculate the cumulative minimum of single-precision floating-point strided array elements.
• scuminabs( N, x, strideX, y, strideY ): calculate the cumulative minimum absolute value of single-precision floating-point strided array elements.
• sdsmean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation.
• sdsmeanors( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation with extended accumulation.
• sdsnanmean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using extended accumulation.
• sdsnanmeanors( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using ordinary recursive summation with extended accumulation.
• smax( N, x, stride ): calculate the maximum value of a single-precision floating-point strided array.
• smaxabs( N, x, stride ): calculate the maximum absolute value of a single-precision floating-point strided array.
• smaxsorted( N, x, stride ): calculate the maximum value of a sorted single-precision floating-point strided array.
• smean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array.
• smeankbn( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using an improved Kahan–Babuška algorithm.
• smeankbn2( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm.
• smeanli( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using a one-pass trial mean algorithm.
• smeanlipw( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using a one-pass trial mean algorithm with pairwise summation.
• smeanors( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation.
• smeanpn( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm.
• smeanpw( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using pairwise summation.
• smeanwd( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using Welford's algorithm.
• smin( N, x, stride ): calculate the minimum value of a single-precision floating-point strided array.
• sminabs( N, x, stride ): calculate the minimum absolute value of a single-precision floating-point strided array.
• sminsorted( N, x, stride ): calculate the minimum value of a sorted single-precision floating-point strided array.
• snanmax( N, x, stride ): calculate the maximum value of a single-precision floating-point strided array, ignoring NaN values.
• snanmaxabs( N, x, stride ): calculate the maximum absolute value of a single-precision floating-point strided array, ignoring NaN values.
• snanmean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values.
• snanmeanors( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using ordinary recursive summation.
• snanmeanpn( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using a two-pass error correction algorithm.
• snanmeanwd( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using Welford's algorithm.
• snanmin( N, x, stride ): calculate the minimum value of a single-precision floating-point strided array, ignoring NaN values.
• snanminabs( N, x, stride ): calculate the minimum absolute value of a single-precision floating-point strided array, ignoring NaN values.
• snanrange( N, x, stride ): calculate the range of a single-precision floating-point strided array, ignoring NaN values.
• srange( N, x, stride ): calculate the range of a single-precision floating-point strided array.
• sstdev( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array.
• sstdevpn( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array using a two-pass algorithm.
• sstdevtk( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array using a one-pass textbook algorithm.
• sstdevwd( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array using Welford's algorithm.
• sstdevyc( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.
• svariance( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array.
• svariancepn( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array using a two-pass algorithm.
• svariancetk( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array using a one-pass textbook algorithm.
• svariancewd( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array using Welford's algorithm.
• svarianceyc( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.
• variance( N, correction, x, stride ): calculate the variance of a strided array.
• variancepn( N, correction, x, stride ): calculate the variance of a strided array using a two-pass algorithm.

Examples

var objectKeys = require( '@stdlib/utils/keys' );
var ns = require( '@stdlib/stats/base' );

console.log( objectKeys( ns ) );