From 4e737d5e70f5068d49fd26afe6f992bf97ca8ad3 Mon Sep 17 00:00:00 2001 From: Planeshifter <1913638+Planeshifter@users.noreply.github.com> Date: Sun, 5 Jul 2026 03:27:49 +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/blas/ext/base/ndarray/README.md | 6 +++--- lib/node_modules/@stdlib/stats/base/dists/planck/README.md | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/lib/node_modules/@stdlib/blas/ext/base/ndarray/README.md b/lib/node_modules/@stdlib/blas/ext/base/ndarray/README.md index bf14e6d39a2b..66f826a30866 100644 --- a/lib/node_modules/@stdlib/blas/ext/base/ndarray/README.md +++ b/lib/node_modules/@stdlib/blas/ext/base/ndarray/README.md @@ -82,8 +82,8 @@ The namespace exposes the following APIs: - [`dsum( arrays )`][@stdlib/blas/ext/base/ndarray/dsum]: compute the sum of all elements in a one-dimensional double-precision floating-point ndarray. - [`dsumkbn( arrays )`][@stdlib/blas/ext/base/ndarray/dsumkbn]: compute the sum of all elements in a one-dimensional double-precision floating-point ndarray using an improved Kahan–Babuška algorithm. - [`dsumkbn2( arrays )`][@stdlib/blas/ext/base/ndarray/dsumkbn2]: compute the sum of all elements in a one-dimensional double-precision floating-point ndarray using a second-order iterative Kahan–Babuška algorithm. -- [`dsumors( arrays )`][@stdlib/blas/ext/base/ndarray/dsumors]: compute the sum of a one-dimensional double-precision floating-point ndarray using ordinary recursive summation. -- [`dsumpw( arrays )`][@stdlib/blas/ext/base/ndarray/dsumpw]: compute the sum of a one-dimensional double-precision floating-point ndarray using pairwise summation. +- [`dsumors( arrays )`][@stdlib/blas/ext/base/ndarray/dsumors]: compute the sum of all elements in a one-dimensional double-precision floating-point ndarray using ordinary recursive summation. +- [`dsumpw( arrays )`][@stdlib/blas/ext/base/ndarray/dsumpw]: compute the sum of all elements in a one-dimensional double-precision floating-point ndarray using pairwise summation. - [`dunitspace( arrays )`][@stdlib/blas/ext/base/ndarray/dunitspace]: fill a one-dimensional double-precision floating-point ndarray with linearly spaced numeric elements which increment by `1` starting from a specified value. - [`dxpy( arrays )`][@stdlib/blas/ext/base/ndarray/dxpy]: add elements of a one-dimensional double-precision floating-point ndarray to the corresponding elements of a second one-dimensional double-precision floating-point ndarray and assign the results to the second ndarray. - [`dxsa( arrays )`][@stdlib/blas/ext/base/ndarray/dxsa]: subtract a scalar constant from each element in a one-dimensional double-precision floating-point ndarray. @@ -146,7 +146,7 @@ The namespace exposes the following APIs: - [`ssum( arrays )`][@stdlib/blas/ext/base/ndarray/ssum]: compute the sum of all elements in a one-dimensional single-precision floating-point ndarray. - [`ssumkbn( arrays )`][@stdlib/blas/ext/base/ndarray/ssumkbn]: compute the sum of all elements in a one-dimensional single-precision floating-point ndarray using an improved Kahan–Babuška algorithm. - [`ssumkbn2( arrays )`][@stdlib/blas/ext/base/ndarray/ssumkbn2]: compute the sum of all elements in a one-dimensional single-precision floating-point ndarray using a second-order iterative Kahan–Babuška algorithm. -- [`ssumors( arrays )`][@stdlib/blas/ext/base/ndarray/ssumors]: compute the sum of a one-dimensional single-precision floating-point ndarray using ordinary recursive summation. +- [`ssumors( arrays )`][@stdlib/blas/ext/base/ndarray/ssumors]: compute the sum of all elements in a one-dimensional single-precision floating-point ndarray using ordinary recursive summation. - [`ssumpw( arrays )`][@stdlib/blas/ext/base/ndarray/ssumpw]: compute the sum of all elements in a one-dimensional single-precision floating-point ndarray using pairwise summation. - [`sunitspace( arrays )`][@stdlib/blas/ext/base/ndarray/sunitspace]: fill a one-dimensional single-precision floating-point ndarray with linearly spaced numeric elements which increment by `1` starting from a specified value. - [`sxpy( arrays )`][@stdlib/blas/ext/base/ndarray/sxpy]: add elements of a one-dimensional single-precision floating-point ndarray to the corresponding elements of a second one-dimensional single-precision floating-point ndarray and assign the results to the second ndarray. diff --git a/lib/node_modules/@stdlib/stats/base/dists/planck/README.md b/lib/node_modules/@stdlib/stats/base/dists/planck/README.md index 923c8c8d0c85..4a11871a5cde 100644 --- a/lib/node_modules/@stdlib/stats/base/dists/planck/README.md +++ b/lib/node_modules/@stdlib/stats/base/dists/planck/README.md @@ -64,12 +64,12 @@ The namespace contains the following functions for calculating distribution prop - [`entropy( lambda )`][@stdlib/stats/base/dists/planck/entropy]: planck (discrete exponential) distribution differential entropy. - [`kurtosis( lambda )`][@stdlib/stats/base/dists/planck/kurtosis]: planck (discrete exponential) distribution excess kurtosis. -- [`mean( lambda )`][@stdlib/stats/base/dists/planck/mean]: planck distribution expected value. +- [`mean( lambda )`][@stdlib/stats/base/dists/planck/mean]: planck (discrete exponential) distribution expected value. - [`median( lambda )`][@stdlib/stats/base/dists/planck/median]: planck (discrete exponential) distribution median. - [`mode( lambda )`][@stdlib/stats/base/dists/planck/mode]: planck (discrete exponential) distribution mode. - [`skewness( lambda )`][@stdlib/stats/base/dists/planck/skewness]: planck (discrete exponential) distribution skewness. -- [`stdev( lambda )`][@stdlib/stats/base/dists/planck/stdev]: planck distribution standard deviation. -- [`variance( lambda )`][@stdlib/stats/base/dists/planck/variance]: planck distribution variance. +- [`stdev( lambda )`][@stdlib/stats/base/dists/planck/stdev]: planck (discrete exponential) distribution standard deviation. +- [`variance( lambda )`][@stdlib/stats/base/dists/planck/variance]: planck (discrete exponential) distribution variance.