docs: update namespace table of contents

PR-URL: #10003
Reviewed-by: Athan Reines <kgryte@gmail.com>

Author: stdlib-bot

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

@@ -117,15 +117,38 @@ Other statistical functions included are:
 -   <span class="signature">[`maxBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/max-by]</span><span class="delimiter">: </span><span class="description">compute the maximum value along one or more ndarray dimensions according to a callback function.</span>
 -   <span class="signature">[`max( x[, options] )`][@stdlib/stats/max]</span><span class="delimiter">: </span><span class="description">compute the maximum value along one or more ndarray dimensions.</span>
 -   <span class="signature">[`maxabs( x[, options] )`][@stdlib/stats/maxabs]</span><span class="delimiter">: </span><span class="description">compute the maximum absolute value along one or more ndarray dimensions.</span>
+-   <span class="signature">[`maxsorted( x[, options] )`][@stdlib/stats/maxsorted]</span><span class="delimiter">: </span><span class="description">compute the maximum value along one or more sorted ndarray dimensions.</span>
 -   <span class="signature">[`mean( x[, options] )`][@stdlib/stats/mean]</span><span class="delimiter">: </span><span class="description">compute the arithmetic mean along one or more ndarray dimensions.</span>
+-   <span class="signature">[`meankbn( x[, options] )`][@stdlib/stats/meankbn]</span><span class="delimiter">: </span><span class="description">compute the arithmetic mean along one or more ndarray dimensions using an improved Kahan–Babuška algorithm.</span>
+-   <span class="signature">[`meankbn2( x[, options] )`][@stdlib/stats/meankbn2]</span><span class="delimiter">: </span><span class="description">compute the arithmetic mean along one or more ndarray dimensions using a second-order iterative Kahan–Babuška algorithm.</span>
+-   <span class="signature">[`meanors( x[, options] )`][@stdlib/stats/meanors]</span><span class="delimiter">: </span><span class="description">compute the arithmetic mean along one or more ndarray dimensions using ordinary recursive summation.</span>
+-   <span class="signature">[`meanpn( x[, options] )`][@stdlib/stats/meanpn]</span><span class="delimiter">: </span><span class="description">compute the arithmetic mean along one or more ndarray dimensions using a two-pass error correction algorithm.</span>
+-   <span class="signature">[`meanpw( x[, options] )`][@stdlib/stats/meanpw]</span><span class="delimiter">: </span><span class="description">compute the arithmetic mean along one or more ndarray dimensions using pairwise summation.</span>
+-   <span class="signature">[`meanwd( x[, options] )`][@stdlib/stats/meanwd]</span><span class="delimiter">: </span><span class="description">compute the arithmetic mean along one or more ndarray dimensions using Welford's algorithm.</span>
+-   <span class="signature">[`mediansorted( x[, options] )`][@stdlib/stats/mediansorted]</span><span class="delimiter">: </span><span class="description">compute the median value along one or more sorted ndarray dimensions.</span>
+-   <span class="signature">[`midrangeBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/midrange-by]</span><span class="delimiter">: </span><span class="description">compute the mid-range along one or more ndarray dimensions according to a callback function.</span>
+-   <span class="signature">[`midrange( x[, options] )`][@stdlib/stats/midrange]</span><span class="delimiter">: </span><span class="description">compute the mid-range along one or more ndarray dimensions.</span>
 -   <span class="signature">[`minBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/min-by]</span><span class="delimiter">: </span><span class="description">compute the minimum value along one or more ndarray dimensions according to a callback function.</span>
 -   <span class="signature">[`min( x[, options] )`][@stdlib/stats/min]</span><span class="delimiter">: </span><span class="description">compute the minimum value along one or more ndarray dimensions.</span>
 -   <span class="signature">[`minabs( x[, options] )`][@stdlib/stats/minabs]</span><span class="delimiter">: </span><span class="description">compute the minimum absolute value along one or more ndarray dimensions.</span>
+-   <span class="signature">[`minsorted( x[, options] )`][@stdlib/stats/minsorted]</span><span class="delimiter">: </span><span class="description">compute the minimum value along one or more sorted ndarray dimensions.</span>
+-   <span class="signature">[`nanmaxBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/nanmax-by]</span><span class="delimiter">: </span><span class="description">compute the maximum value along one or more ndarray dimensions according to a callback function, ignoring `NaN` values.</span>
 -   <span class="signature">[`nanmax( x[, options] )`][@stdlib/stats/nanmax]</span><span class="delimiter">: </span><span class="description">compute the maximum value along one or more ndarray dimensions, ignoring `NaN` values.</span>
+-   <span class="signature">[`nanmaxabs( x[, options] )`][@stdlib/stats/nanmaxabs]</span><span class="delimiter">: </span><span class="description">compute the maximum absolute value along one or more ndarray dimensions, ignoring `NaN` values.</span>
 -   <span class="signature">[`nanmean( x[, options] )`][@stdlib/stats/nanmean]</span><span class="delimiter">: </span><span class="description">compute the arithmetic mean along one or more ndarray dimensions, ignoring `NaN` values.</span>
+-   <span class="signature">[`nanmeanors( x[, options] )`][@stdlib/stats/nanmeanors]</span><span class="delimiter">: </span><span class="description">compute the arithmetic mean along one or more ndarray dimensions, ignoring `NaN` values and using ordinary recursive summation.</span>
+-   <span class="signature">[`nanmeanpn( x[, options] )`][@stdlib/stats/nanmeanpn]</span><span class="delimiter">: </span><span class="description">compute the arithmetic mean along one or more ndarray dimensions, ignoring `NaN` values and using a two-pass error correction algorithm.</span>
+-   <span class="signature">[`nanmeanwd( x[, options] )`][@stdlib/stats/nanmeanwd]</span><span class="delimiter">: </span><span class="description">compute the arithmetic mean along one or more ndarray dimensions, ignoring `NaN` values and using Welford's algorithm.</span>
+-   <span class="signature">[`nanmidrangeBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/nanmidrange-by]</span><span class="delimiter">: </span><span class="description">compute the **mid-range** along one or more ndarray dimensions according to a callback function, ignoring `NaN` values.</span>
+-   <span class="signature">[`nanminBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/nanmin-by]</span><span class="delimiter">: </span><span class="description">compute the minimum value along one or more ndarray dimensions according to a callback function, ignoring `NaN` values.</span>
 -   <span class="signature">[`nanmin( x[, options] )`][@stdlib/stats/nanmin]</span><span class="delimiter">: </span><span class="description">compute the minimum value along one or more ndarray dimensions, ignoring `NaN` values.</span>
+-   <span class="signature">[`nanminabs( x[, options] )`][@stdlib/stats/nanminabs]</span><span class="delimiter">: </span><span class="description">compute the minimum absolute value along one or more ndarray dimensions, ignoring `NaN` values.</span>
+-   <span class="signature">[`nanrangeBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/nanrange-by]</span><span class="delimiter">: </span><span class="description">compute the **range** along one or more ndarray dimensions according to a callback function, ignoring `NaN` values.</span>
+-   <span class="signature">[`nanrange( x[, options] )`][@stdlib/stats/nanrange]</span><span class="delimiter">: </span><span class="description">compute the range along one or more ndarray dimensions, ignoring `NaN` values.</span>
 -   <span class="signature">[`padjust( pvals, method[, comparisons] )`][@stdlib/stats/padjust]</span><span class="delimiter">: </span><span class="description">adjust supplied p-values for multiple comparisons.</span>
+-   <span class="signature">[`rangeBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/range-by]</span><span class="delimiter">: </span><span class="description">compute the **range** along one or more ndarray dimensions according to a callback function.</span>
 -   <span class="signature">[`range( x[, options] )`][@stdlib/stats/range]</span><span class="delimiter">: </span><span class="description">compute the range along one or more ndarray dimensions.</span>
+-   <span class="signature">[`rangeabs( x[, options] )`][@stdlib/stats/rangeabs]</span><span class="delimiter">: </span><span class="description">compute the range of absolute values along one or more ndarray dimensions.</span>
 -   <span class="signature">[`ranks( arr[, opts] )`][@stdlib/stats/ranks]</span><span class="delimiter">: </span><span class="description">compute ranks for values of an array-like object.</span>
 
 </div>
@@ -183,24 +206,70 @@ console.log( objectKeys( statistics ) );
 
 [@stdlib/stats/maxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/maxabs
 
+[@stdlib/stats/maxsorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/maxsorted
+
 [@stdlib/stats/mean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/mean
 
+[@stdlib/stats/meankbn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/meankbn
+
+[@stdlib/stats/meankbn2]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/meankbn2
+
+[@stdlib/stats/meanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/meanors
+
+[@stdlib/stats/meanpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/meanpn
+
+[@stdlib/stats/meanpw]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/meanpw
+
+[@stdlib/stats/meanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/meanwd
+
+[@stdlib/stats/mediansorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/mediansorted
+
+[@stdlib/stats/midrange-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/midrange-by
+
+[@stdlib/stats/midrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/midrange
+
 [@stdlib/stats/min-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/min-by
 
 [@stdlib/stats/min]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/min
 
 [@stdlib/stats/minabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/minabs
 
+[@stdlib/stats/minsorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/minsorted
+
+[@stdlib/stats/nanmax-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanmax-by
+
 [@stdlib/stats/nanmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanmax
 
+[@stdlib/stats/nanmaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanmaxabs
+
 [@stdlib/stats/nanmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanmean
 
+[@stdlib/stats/nanmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanmeanors
+
+[@stdlib/stats/nanmeanpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanmeanpn
+
+[@stdlib/stats/nanmeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanmeanwd
+
+[@stdlib/stats/nanmidrange-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanmidrange-by
+
+[@stdlib/stats/nanmin-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanmin-by
+
 [@stdlib/stats/nanmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanmin
 
+[@stdlib/stats/nanminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanminabs
+
+[@stdlib/stats/nanrange-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanrange-by
+
+[@stdlib/stats/nanrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/nanrange
+
 [@stdlib/stats/padjust]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/padjust
 
+[@stdlib/stats/range-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/range-by
+
 [@stdlib/stats/range]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/range
 
+[@stdlib/stats/rangeabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/rangeabs
+
 [@stdlib/stats/ranks]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/ranks
 
 [@stdlib/stats/base]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base

lib/node_modules/@stdlib/stats/base/ndarray/README.md

@@ -94,7 +94,9 @@ The namespace exposes the following APIs:
 -   <span class="signature">[`dstdev( arrays )`][@stdlib/stats/base/ndarray/dstdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional double-precision floating-point ndarray.</span>
 -   <span class="signature">[`dstdevch( arrays )`][@stdlib/stats/base/ndarray/dstdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm.</span>
 -   <span class="signature">[`dstdevpn( arrays )`][@stdlib/stats/base/ndarray/dstdevpn]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a two-pass algorithm.</span>
+-   <span class="signature">[`dstdevtk( arrays )`][@stdlib/stats/base/ndarray/dstdevtk]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass textbook algorithm.</span>
 -   <span class="signature">[`dstdevwd( arrays )`][@stdlib/stats/base/ndarray/dstdevwd]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using Welford's algorithm.</span>
+-   <span class="signature">[`dstdevyc( arrays )`][@stdlib/stats/base/ndarray/dstdevyc]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass algorithm proposed by Youngs and Cramer.</span>
 -   <span class="signature">[`dztest( arrays )`][@stdlib/stats/base/ndarray/dztest]</span><span class="delimiter">: </span><span class="description">compute a one-sample Z-test for a one-dimensional double-precision floating-point ndarray.</span>
 -   <span class="signature">[`dztest2( arrays )`][@stdlib/stats/base/ndarray/dztest2]</span><span class="delimiter">: </span><span class="description">compute a two-sample Z-test for two one-dimensional double-precision floating-point ndarrays.</span>
 -   <span class="signature">[`maxBy( arrays, clbk[, thisArg ] )`][@stdlib/stats/base/ndarray/max-by]</span><span class="delimiter">: </span><span class="description">compute the maximum value of a one-dimensional ndarray via a callback function.</span>
@@ -189,7 +191,9 @@ The namespace exposes the following APIs:
 -   <span class="signature">[`sstdev( arrays )`][@stdlib/stats/base/ndarray/sstdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional single-precision floating-point ndarray.</span>
 -   <span class="signature">[`sstdevch( arrays )`][@stdlib/stats/base/ndarray/sstdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a one-pass trial mean algorithm.</span>
 -   <span class="signature">[`sstdevpn( arrays )`][@stdlib/stats/base/ndarray/sstdevpn]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a two-pass algorithm.</span>
+-   <span class="signature">[`sstdevtk( arrays )`][@stdlib/stats/base/ndarray/sstdevtk]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a one-pass textbook algorithm.</span>
 -   <span class="signature">[`sstdevwd( arrays )`][@stdlib/stats/base/ndarray/sstdevwd]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using Welford's algorithm.</span>
+-   <span class="signature">[`sstdevyc( arrays )`][@stdlib/stats/base/ndarray/sstdevyc]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a one-pass algorithm proposed by Youngs and Cramer.</span>
 -   <span class="signature">[`stdev( arrays )`][@stdlib/stats/base/ndarray/stdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional ndarray.</span>
 -   <span class="signature">[`stdevch( arrays )`][@stdlib/stats/base/ndarray/stdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional ndarray using a one-pass trial mean algorithm.</span>
 -   <span class="signature">[`stdevpn( arrays )`][@stdlib/stats/base/ndarray/stdevpn]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a one-dimensional ndarray using a two-pass algorithm.</span>
@@ -343,8 +347,12 @@ console.log( objectKeys( ns ) );
 
 [@stdlib/stats/base/ndarray/dstdevpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dstdevpn
 
+[@stdlib/stats/base/ndarray/dstdevtk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dstdevtk
+
 [@stdlib/stats/base/ndarray/dstdevwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dstdevwd
 
+[@stdlib/stats/base/ndarray/dstdevyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dstdevyc
+
 [@stdlib/stats/base/ndarray/dztest]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dztest
 
 [@stdlib/stats/base/ndarray/dztest2]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dztest2
@@ -533,8 +541,12 @@ console.log( objectKeys( ns ) );
 
 [@stdlib/stats/base/ndarray/sstdevpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sstdevpn
 
+[@stdlib/stats/base/ndarray/sstdevtk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sstdevtk
+
 [@stdlib/stats/base/ndarray/sstdevwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sstdevwd
 
+[@stdlib/stats/base/ndarray/sstdevyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sstdevyc
+
 [@stdlib/stats/base/ndarray/stdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/stdev
 
 [@stdlib/stats/base/ndarray/stdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/stdevch