Update examples

Author: kgryte

lib/node_modules/@stdlib/stats/incr/vmr/README.md

@@ -124,6 +124,8 @@ D = accumulator();
 
     Accordingly, one can use the [variance-to-mean ratio][variance-to-mean-ratio] to assess whether observed data can be modeled as a Poisson process. When observed data is "under-dispersed", observed data may be more regular than as would be the case for a Poisson process. When observed data is "over-dispersed", observed data may contain clusters (i.e., clumped, concentrated data).
 
+-   The [variance-to-mean ratio][variance-to-mean-ratio] is typically computed on non-negative values. The measure may lack meaning for data which can assume both positive and negative values.
+
 -   The [variance-to-mean ratio][variance-to-mean-ratio] is also known as the **index of dispersion**, **dispersion index**, **coefficient of dispersion**, and **relative variance**.
 
 </section>

lib/node_modules/@stdlib/stats/incr/vmr/docs/repl.txt

@@ -27,10 +27,10 @@
     null
     > D = accumulator( 2.0 )
     0.0
-    > D = accumulator( -4.0 )
-    -24.5
+    > D = accumulator( 1.0 )
+    ~0.33
     > D = accumulator()
-    -24.5
+    ~0.33
 
     See Also
     --------

lib/node_modules/@stdlib/stats/incr/vmr/lib/index.js

@@ -34,11 +34,11 @@
 * D = accumulator( 2.0 );
 * // returns 0.0
 *
-* D = accumulator( -4.0 );
-* // returns -24.5
+* D = accumulator( 1.0 );
+* // returns ~0.33
 *
 * D = accumulator();
-* // returns -24.5
+* // returns ~0.33
 */
 
 // MODULES //

lib/node_modules/@stdlib/stats/incr/vmr/lib/main.js

@@ -78,11 +78,11 @@ var isnan = require( '@stdlib/math/base/assert/is-nan' );
 * D = accumulator( 2.0 );
 * // returns 0.0
 *
-* D = accumulator( -4.0 );
-* // returns -24.5
+* D = accumulator( 1.0 );
+* // returns ~0.33
 *
 * D = accumulator();
-* // returns -24.5
+* // returns ~0.33
 *
 * @example
 * var accumulator = incrvmr( 3.14 );