From 64b59f8f8e9161a66e1f8e0587d4d3ec728e6ec4 Mon Sep 17 00:00:00 2001 From: Planeshifter <1913638+Planeshifter@users.noreply.github.com> Date: Sat, 31 Jan 2026 03:58:52 +0000 Subject: [PATCH] docs: update namespace table of contents Signed-off-by: stdlib-bot <82920195+stdlib-bot@users.noreply.github.com> --- lib/node_modules/@stdlib/stats/README.md | 69 +++++++++++++++++++ .../@stdlib/stats/base/ndarray/README.md | 12 ++++ 2 files changed, 81 insertions(+) diff --git a/lib/node_modules/@stdlib/stats/README.md b/lib/node_modules/@stdlib/stats/README.md index dea4ee206acb..7e1a1469a0a2 100644 --- a/lib/node_modules/@stdlib/stats/README.md +++ b/lib/node_modules/@stdlib/stats/README.md @@ -117,15 +117,38 @@ Other statistical functions included are: - [`maxBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/max-by]: compute the maximum value along one or more ndarray dimensions according to a callback function. - [`max( x[, options] )`][@stdlib/stats/max]: compute the maximum value along one or more ndarray dimensions. - [`maxabs( x[, options] )`][@stdlib/stats/maxabs]: compute the maximum absolute value along one or more ndarray dimensions. +- [`maxsorted( x[, options] )`][@stdlib/stats/maxsorted]: compute the maximum value along one or more sorted ndarray dimensions. - [`mean( x[, options] )`][@stdlib/stats/mean]: compute the arithmetic mean along one or more ndarray dimensions. +- [`meankbn( x[, options] )`][@stdlib/stats/meankbn]: compute the arithmetic mean along one or more ndarray dimensions using an improved Kahan–Babuška algorithm. +- [`meankbn2( x[, options] )`][@stdlib/stats/meankbn2]: compute the arithmetic mean along one or more ndarray dimensions using a second-order iterative Kahan–Babuška algorithm. +- [`meanors( x[, options] )`][@stdlib/stats/meanors]: compute the arithmetic mean along one or more ndarray dimensions using ordinary recursive summation. +- [`meanpn( x[, options] )`][@stdlib/stats/meanpn]: compute the arithmetic mean along one or more ndarray dimensions using a two-pass error correction algorithm. +- [`meanpw( x[, options] )`][@stdlib/stats/meanpw]: compute the arithmetic mean along one or more ndarray dimensions using pairwise summation. +- [`meanwd( x[, options] )`][@stdlib/stats/meanwd]: compute the arithmetic mean along one or more ndarray dimensions using Welford's algorithm. +- [`mediansorted( x[, options] )`][@stdlib/stats/mediansorted]: compute the median value along one or more sorted ndarray dimensions. +- [`midrangeBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/midrange-by]: compute the mid-range along one or more ndarray dimensions according to a callback function. +- [`midrange( x[, options] )`][@stdlib/stats/midrange]: compute the mid-range along one or more ndarray dimensions. - [`minBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/min-by]: compute the minimum value along one or more ndarray dimensions according to a callback function. - [`min( x[, options] )`][@stdlib/stats/min]: compute the minimum value along one or more ndarray dimensions. - [`minabs( x[, options] )`][@stdlib/stats/minabs]: compute the minimum absolute value along one or more ndarray dimensions. +- [`minsorted( x[, options] )`][@stdlib/stats/minsorted]: compute the minimum value along one or more sorted ndarray dimensions. +- [`nanmaxBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/nanmax-by]: compute the maximum value along one or more ndarray dimensions according to a callback function, ignoring `NaN` values. - [`nanmax( x[, options] )`][@stdlib/stats/nanmax]: compute the maximum value along one or more ndarray dimensions, ignoring `NaN` values. +- [`nanmaxabs( x[, options] )`][@stdlib/stats/nanmaxabs]: compute the maximum absolute value along one or more ndarray dimensions, ignoring `NaN` values. - [`nanmean( x[, options] )`][@stdlib/stats/nanmean]: compute the arithmetic mean along one or more ndarray dimensions, ignoring `NaN` values. +- [`nanmeanors( x[, options] )`][@stdlib/stats/nanmeanors]: compute the arithmetic mean along one or more ndarray dimensions, ignoring `NaN` values and using ordinary recursive summation. +- [`nanmeanpn( x[, options] )`][@stdlib/stats/nanmeanpn]: compute the arithmetic mean along one or more ndarray dimensions, ignoring `NaN` values and using a two-pass error correction algorithm. +- [`nanmeanwd( x[, options] )`][@stdlib/stats/nanmeanwd]: compute the arithmetic mean along one or more ndarray dimensions, ignoring `NaN` values and using Welford's algorithm. +- [`nanmidrangeBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/nanmidrange-by]: compute the **mid-range** along one or more ndarray dimensions according to a callback function, ignoring `NaN` values. +- [`nanminBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/nanmin-by]: compute the minimum value along one or more ndarray dimensions according to a callback function, ignoring `NaN` values. - [`nanmin( x[, options] )`][@stdlib/stats/nanmin]: compute the minimum value along one or more ndarray dimensions, ignoring `NaN` values. +- [`nanminabs( x[, options] )`][@stdlib/stats/nanminabs]: compute the minimum absolute value along one or more ndarray dimensions, ignoring `NaN` values. +- [`nanrangeBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/nanrange-by]: compute the **range** along one or more ndarray dimensions according to a callback function, ignoring `NaN` values. +- [`nanrange( x[, options] )`][@stdlib/stats/nanrange]: compute the range along one or more ndarray dimensions, ignoring `NaN` values. - [`padjust( pvals, method[, comparisons] )`][@stdlib/stats/padjust]: adjust supplied p-values for multiple comparisons. +- [`rangeBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/range-by]: compute the **range** along one or more ndarray dimensions according to a callback function. - [`range( x[, options] )`][@stdlib/stats/range]: compute the range along one or more ndarray dimensions. +- [`rangeabs( x[, options] )`][@stdlib/stats/rangeabs]: compute the range of absolute values along one or more ndarray dimensions. - [`ranks( arr[, opts] )`][@stdlib/stats/ranks]: compute ranks for values of an array-like object. @@ -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 diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/README.md b/lib/node_modules/@stdlib/stats/base/ndarray/README.md index bade65e4fdec..8723d51770f2 100644 --- a/lib/node_modules/@stdlib/stats/base/ndarray/README.md +++ b/lib/node_modules/@stdlib/stats/base/ndarray/README.md @@ -94,7 +94,9 @@ The namespace exposes the following APIs: - [`dstdev( arrays )`][@stdlib/stats/base/ndarray/dstdev]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray. - [`dstdevch( arrays )`][@stdlib/stats/base/ndarray/dstdevch]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm. - [`dstdevpn( arrays )`][@stdlib/stats/base/ndarray/dstdevpn]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a two-pass algorithm. +- [`dstdevtk( arrays )`][@stdlib/stats/base/ndarray/dstdevtk]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass textbook algorithm. - [`dstdevwd( arrays )`][@stdlib/stats/base/ndarray/dstdevwd]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using Welford's algorithm. +- [`dstdevyc( arrays )`][@stdlib/stats/base/ndarray/dstdevyc]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass algorithm proposed by Youngs and Cramer. - [`dztest( arrays )`][@stdlib/stats/base/ndarray/dztest]: compute a one-sample Z-test for a one-dimensional double-precision floating-point ndarray. - [`dztest2( arrays )`][@stdlib/stats/base/ndarray/dztest2]: compute a two-sample Z-test for two one-dimensional double-precision floating-point ndarrays. - [`maxBy( arrays, clbk[, thisArg ] )`][@stdlib/stats/base/ndarray/max-by]: compute the maximum value of a one-dimensional ndarray via a callback function. @@ -189,7 +191,9 @@ The namespace exposes the following APIs: - [`sstdev( arrays )`][@stdlib/stats/base/ndarray/sstdev]: calculate the standard deviation of a one-dimensional single-precision floating-point ndarray. - [`sstdevch( arrays )`][@stdlib/stats/base/ndarray/sstdevch]: calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a one-pass trial mean algorithm. - [`sstdevpn( arrays )`][@stdlib/stats/base/ndarray/sstdevpn]: calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a two-pass algorithm. +- [`sstdevtk( arrays )`][@stdlib/stats/base/ndarray/sstdevtk]: calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a one-pass textbook algorithm. - [`sstdevwd( arrays )`][@stdlib/stats/base/ndarray/sstdevwd]: calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using Welford's algorithm. +- [`sstdevyc( arrays )`][@stdlib/stats/base/ndarray/sstdevyc]: calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a one-pass algorithm proposed by Youngs and Cramer. - [`stdev( arrays )`][@stdlib/stats/base/ndarray/stdev]: calculate the standard deviation of a one-dimensional ndarray. - [`stdevch( arrays )`][@stdlib/stats/base/ndarray/stdevch]: calculate the standard deviation of a one-dimensional ndarray using a one-pass trial mean algorithm. - [`stdevpn( arrays )`][@stdlib/stats/base/ndarray/stdevpn]: calculate the standard deviation of a one-dimensional ndarray using a two-pass algorithm. @@ -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