From e821269b78eafe3bbcdfb2107c5f3e0bd5e77871 Mon Sep 17 00:00:00 2001 From: Planeshifter <1913638+Planeshifter@users.noreply.github.com> Date: Sun, 8 Jun 2025 02:52:25 +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/base/README.md | 4 +-- .../@stdlib/number/uint16/base/README.md | 9 +++++ .../@stdlib/number/uint8/base/README.md | 9 +++++ .../@stdlib/stats/array/README.md | 9 +++++ lib/node_modules/@stdlib/stats/base/README.md | 22 ++---------- .../@stdlib/stats/strided/README.md | 36 +++++++++++++++++++ 6 files changed, 67 insertions(+), 22 deletions(-) diff --git a/lib/node_modules/@stdlib/blas/base/README.md b/lib/node_modules/@stdlib/blas/base/README.md index 4276b8027ceb..9cdbd9445f4f 100644 --- a/lib/node_modules/@stdlib/blas/base/README.md +++ b/lib/node_modules/@stdlib/blas/base/README.md @@ -74,8 +74,8 @@ var o = blas; - [`isamax( N, x, strideX )`][@stdlib/blas/base/isamax]: find the index of the first element having the maximum absolute value. - [`sasum( N, x, stride )`][@stdlib/blas/base/sasum]: compute the sum of absolute values (_L1_ norm). - [`saxpy( N, alpha, x, strideX, y, strideY )`][@stdlib/blas/base/saxpy]: multiply a vector `x` by a constant `alpha` and add the result to `y`. -- [`scasum( N, cx, strideX )`][@stdlib/blas/base/scasum]: compute the sum of the absolute values of the real and imaginary components of a single-precision complex floating-point vector. -- [`scnrm2( N, cx, strideX )`][@stdlib/blas/base/scnrm2]: compute the L2-norm of a complex single-precision floating-point vector. +- [`scasum( N, x, strideX )`][@stdlib/blas/base/scasum]: compute the sum of the absolute values of the real and imaginary components of a single-precision complex floating-point vector. +- [`scnrm2( N, x, strideX )`][@stdlib/blas/base/scnrm2]: compute the L2-norm of a complex single-precision floating-point vector. - [`scopy( N, x, strideX, y, strideY )`][@stdlib/blas/base/scopy]: copy values from `x` into `y`. - [`sdot( N, x, strideX, y, strideY )`][@stdlib/blas/base/sdot]: calculate the dot product of two single-precision floating-point vectors. - [`sdsdot( N, scalar, x, strideX, y, strideY )`][@stdlib/blas/base/sdsdot]: calculate the dot product of two single-precision floating-point vectors with extended accumulation. diff --git a/lib/node_modules/@stdlib/number/uint16/base/README.md b/lib/node_modules/@stdlib/number/uint16/base/README.md index ce71efce4be3..1fbd1feb0718 100644 --- a/lib/node_modules/@stdlib/number/uint16/base/README.md +++ b/lib/node_modules/@stdlib/number/uint16/base/README.md @@ -43,7 +43,10 @@ var o = ns;
+- [`add( x, y )`][@stdlib/number/uint16/base/add]: compute the sum of two unsigned 16-bit integers. - [`fromBinaryStringUint16( bstr )`][@stdlib/number/uint16/base/from-binary-string]: create an unsigned 16-bit integer from a literal bit representation. +- [`mul( x, y )`][@stdlib/number/uint16/base/mul]: multiply two unsigned 16-bit integers. +- [`sub( x, y )`][@stdlib/number/uint16/base/sub]: subtract two unsigned 16-bit integers. - [`toBinaryStringUint16( x )`][@stdlib/number/uint16/base/to-binary-string]: return a string giving the literal bit representation of an unsigned 16-bit integer.
@@ -87,8 +90,14 @@ console.log( objectKeys( ns ) ); +[@stdlib/number/uint16/base/add]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/number/uint16/base/add + [@stdlib/number/uint16/base/from-binary-string]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/number/uint16/base/from-binary-string +[@stdlib/number/uint16/base/mul]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/number/uint16/base/mul + +[@stdlib/number/uint16/base/sub]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/number/uint16/base/sub + [@stdlib/number/uint16/base/to-binary-string]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/number/uint16/base/to-binary-string diff --git a/lib/node_modules/@stdlib/number/uint8/base/README.md b/lib/node_modules/@stdlib/number/uint8/base/README.md index e2aa2952e4f1..c22d5d4e9493 100644 --- a/lib/node_modules/@stdlib/number/uint8/base/README.md +++ b/lib/node_modules/@stdlib/number/uint8/base/README.md @@ -43,7 +43,10 @@ var o = ns;
+- [`add( x, y )`][@stdlib/number/uint8/base/add]: compute the sum of two unsigned 8-bit integers. - [`fromBinaryStringUint8( bstr )`][@stdlib/number/uint8/base/from-binary-string]: create an unsigned 8-bit integer from a literal bit representation. +- [`mul( x, y )`][@stdlib/number/uint8/base/mul]: multiply two unsigned 8-bit integers. +- [`sub( x, y )`][@stdlib/number/uint8/base/sub]: subtract two unsigned 8-bit integers. - [`toBinaryStringUint8( x )`][@stdlib/number/uint8/base/to-binary-string]: return a string giving the literal bit representation of an unsigned 8-bit integer.
@@ -87,8 +90,14 @@ console.log( objectKeys( ns ) ); +[@stdlib/number/uint8/base/add]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/number/uint8/base/add + [@stdlib/number/uint8/base/from-binary-string]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/number/uint8/base/from-binary-string +[@stdlib/number/uint8/base/mul]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/number/uint8/base/mul + +[@stdlib/number/uint8/base/sub]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/number/uint8/base/sub + [@stdlib/number/uint8/base/to-binary-string]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/number/uint8/base/to-binary-string diff --git a/lib/node_modules/@stdlib/stats/array/README.md b/lib/node_modules/@stdlib/stats/array/README.md index 6b13d45a0254..b69867155f12 100644 --- a/lib/node_modules/@stdlib/stats/array/README.md +++ b/lib/node_modules/@stdlib/stats/array/README.md @@ -71,8 +71,11 @@ The namespace exports the following: - [`nanmaxBy( x, clbk[, thisArg] )`][@stdlib/stats/array/nanmax-by]: calculate the maximum value of an array via a callback function, ignoring `NaN` values. - [`nanmax( x )`][@stdlib/stats/array/nanmax]: calculate the maximum value of an array, ignoring `NaN` values. - [`nanmaxabs( x )`][@stdlib/stats/array/nanmaxabs]: calculate the maximum absolute value of an array, ignoring `NaN` values. +- [`nanminBy( x, clbk[, thisArg] )`][@stdlib/stats/array/nanmin-by]: calculate the minimum value of an array via a callback function, ignoring `NaN` values. - [`nanmin( x )`][@stdlib/stats/array/nanmin]: calculate the minimum value of an array, ignoring `NaN` values. +- [`nanminabs( x )`][@stdlib/stats/array/nanminabs]: calculate the minimum absolute value of an array, ignoring `NaN` values. - [`nanrange( x )`][@stdlib/stats/array/nanrange]: calculate the range of an array, ignoring `NaN` values. +- [`range( x )`][@stdlib/stats/array/range]: calculate the range of an array. - [`varianceyc( x[, correction] )`][@stdlib/stats/array/varianceyc]: calculate the variance of an array using a one-pass algorithm proposed by Youngs and Cramer. @@ -158,10 +161,16 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/array/nanmaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/nanmaxabs +[@stdlib/stats/array/nanmin-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/nanmin-by + [@stdlib/stats/array/nanmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/nanmin +[@stdlib/stats/array/nanminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/nanminabs + [@stdlib/stats/array/nanrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/nanrange +[@stdlib/stats/array/range]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/range + [@stdlib/stats/array/varianceyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/varianceyc diff --git a/lib/node_modules/@stdlib/stats/base/README.md b/lib/node_modules/@stdlib/stats/base/README.md index 6e291cd85970..4faeac56e312 100644 --- a/lib/node_modules/@stdlib/stats/base/README.md +++ b/lib/node_modules/@stdlib/stats/base/README.md @@ -65,16 +65,13 @@ The namespace contains the following statistical functions: - [`dmeanstdevpn( N, correction, x, strideX, out, strideOut )`][@stdlib/stats/base/dmeanstdevpn]: calculate the mean and standard deviation of a double-precision floating-point strided array using a two-pass algorithm. - [`dmeanvar( N, correction, x, strideX, out, strideOut )`][@stdlib/stats/base/dmeanvar]: calculate the mean and variance of a double-precision floating-point strided array. - [`dmeanvarpn( N, correction, x, strideX, out, strideOut )`][@stdlib/stats/base/dmeanvarpn]: calculate the mean and variance of a double-precision floating-point strided array using a two-pass algorithm. -- [`dsem( N, correction, x, strideX )`][@stdlib/stats/strided/dsem]: calculate the standard error of the mean of a double-precision floating-point strided array. -- [`dsempn( N, correction, x, strideX )`][@stdlib/stats/strided/dsempn]: calculate the standard error of the mean of a double-precision floating-point strided array using a two-pass algorithm. -- [`dstdev( N, correction, x, strideX )`][@stdlib/stats/strided/dstdev]: calculate the standard deviation of a double-precision floating-point strided array. - [`dvarm( N, mean, correction, x, stride )`][@stdlib/stats/base/dvarm]: calculate the variance of a double-precision floating-point strided array provided a known mean. - [`dvarmpn( N, mean, correction, x, stride )`][@stdlib/stats/base/dvarmpn]: calculate the variance of a double-precision floating-point strided array provided a known mean and using Neely's correction algorithm. - [`maxBy( N, x, strideX, clbk[, thisArg] )`][@stdlib/stats/base/max-by]: calculate the maximum value of a strided array via a callback function. - [`max( N, x, strideX )`][@stdlib/stats/base/max]: calculate the maximum value of a strided array. - [`maxabs( N, x, strideX )`][@stdlib/stats/base/maxabs]: calculate the maximum absolute value of a strided array. - [`maxsorted( N, x, strideX )`][@stdlib/stats/base/maxsorted]: calculate the maximum value of a sorted strided array. -- [`mean( N, x, stride )`][@stdlib/stats/base/mean]: calculate the arithmetic mean of a strided array. +- [`mean( N, x, strideX )`][@stdlib/stats/base/mean]: calculate the arithmetic mean of a strided array. - [`meankbn( N, x, stride )`][@stdlib/stats/base/meankbn]: calculate the arithmetic mean of a strided array using an improved Kahan–Babuška algorithm. - [`meankbn2( N, x, stride )`][@stdlib/stats/base/meankbn2]: calculate the arithmetic mean of a strided array using a second-order iterative Kahan–Babuška algorithm. - [`meanors( N, x, stride )`][@stdlib/stats/base/meanors]: calculate the arithmetic mean of a strided array using ordinary recursive summation. @@ -96,7 +93,7 @@ The namespace contains the following statistical functions: - [`nanmeanors( N, x, stride )`][@stdlib/stats/base/nanmeanors]: calculate the arithmetic mean of a strided array, ignoring `NaN` values and using ordinary recursive summation. - [`nanmeanpn( N, x, strideX )`][@stdlib/stats/base/nanmeanpn]: calculate the arithmetic mean of a strided array, ignoring `NaN` values and using a two-pass error correction algorithm. - [`nanmeanwd( N, x, stride )`][@stdlib/stats/base/nanmeanwd]: calculate the arithmetic mean of a strided array, ignoring `NaN` values and using Welford's algorithm. -- [`nanminBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/nanmin-by]: calculate the minimum value of a strided array via a callback function, ignoring `NaN` values. +- [`nanminBy( N, x, strideX, clbk[, thisArg] )`][@stdlib/stats/base/nanmin-by]: calculate the minimum value of a strided array via a callback function, ignoring `NaN` values. - [`nanmin( N, x, strideX )`][@stdlib/stats/base/nanmin]: calculate the minimum value of a strided array, ignoring `NaN` values. - [`nanminabs( N, x, strideX )`][@stdlib/stats/base/nanminabs]: calculate the minimum absolute value of a strided array, ignoring `NaN` values. - [`nanmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/nanmskmax]: calculate the maximum value of a strided array according to a mask, ignoring `NaN` values. @@ -128,9 +125,6 @@ The namespace contains the following statistical functions: - [`smeanors( N, x, stride )`][@stdlib/stats/base/smeanors]: calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation. - [`smeanpn( N, x, strideX )`][@stdlib/stats/base/smeanpn]: calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm. - [`snanmean( N, x, stride )`][@stdlib/stats/base/snanmean]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values. -- [`snanmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/snanmskmax]: calculate the maximum value of a single-precision floating-point strided array according to a mask, ignoring `NaN` values. -- [`snanmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/snanmskmin]: calculate the minimum value of a single-precision floating-point strided array according to a mask, ignoring `NaN` values. -- [`snanmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/snanmskrange]: calculate the range of a single-precision floating-point strided array according to a mask, ignoring `NaN` values. - [`snanstdev( N, correction, x, stride )`][@stdlib/stats/base/snanstdev]: calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values. - [`snanstdevch( N, correction, x, stride )`][@stdlib/stats/base/snanstdevch]: calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass trial mean algorithm. - [`snanstdevpn( N, correction, x, stride )`][@stdlib/stats/base/snanstdevpn]: calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm. @@ -225,12 +219,6 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/dmeanvarpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/dmeanvarpn -[@stdlib/stats/strided/dsem]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsem - -[@stdlib/stats/strided/dsempn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsempn - -[@stdlib/stats/strided/dstdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dstdev - [@stdlib/stats/base/dvarm]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/dvarm [@stdlib/stats/base/dvarmpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/dvarmpn @@ -351,12 +339,6 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/snanmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/snanmean -[@stdlib/stats/strided/snanmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmskmax - -[@stdlib/stats/strided/snanmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmskmin - -[@stdlib/stats/strided/snanmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmskrange - [@stdlib/stats/base/snanstdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/snanstdev [@stdlib/stats/base/snanstdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/snanstdevch diff --git a/lib/node_modules/@stdlib/stats/strided/README.md b/lib/node_modules/@stdlib/stats/strided/README.md index abcbb9da8e8b..8008935991eb 100644 --- a/lib/node_modules/@stdlib/stats/strided/README.md +++ b/lib/node_modules/@stdlib/stats/strided/README.md @@ -63,11 +63,13 @@ The namespace exports the following: - [`dmaxabs( N, x, strideX )`][@stdlib/stats/strided/dmaxabs]: calculate the maximum absolute value of a double-precision floating-point strided array. - [`dmaxabssorted( N, x, strideX )`][@stdlib/stats/strided/dmaxabssorted]: calculate the maximum absolute value of a sorted double-precision floating-point strided array. - [`dmaxsorted( N, x, strideX )`][@stdlib/stats/strided/dmaxsorted]: calculate the maximum value of a sorted double-precision floating-point strided array. +- [`dmean( N, x, strideX )`][@stdlib/stats/strided/dmean]: calculate the arithmetic mean of a double-precision floating-point strided array. - [`dmeankbn( N, x, strideX )`][@stdlib/stats/strided/dmeankbn]: calculate the arithmetic mean of a double-precision floating-point strided array using an improved Kahan–Babuška algorithm. - [`dmeankbn2( N, x, strideX )`][@stdlib/stats/strided/dmeankbn2]: calculate the arithmetic mean of a double-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm. - [`dmeanli( N, x, strideX )`][@stdlib/stats/strided/dmeanli]: calculate the arithmetic mean of a double-precision floating-point strided array using a one-pass trial mean algorithm. - [`dmeanlipw( N, x, strideX )`][@stdlib/stats/strided/dmeanlipw]: 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, strideX )`][@stdlib/stats/strided/dmeanors]: calculate the arithmetic mean of a double-precision floating-point strided array using ordinary recursive summation. +- [`dmeanpn( N, x, strideX )`][@stdlib/stats/strided/dmeanpn]: calculate the arithmetic mean of a double-precision floating-point strided array using a two-pass error correction algorithm. - [`dmeanpw( N, x, strideX )`][@stdlib/stats/strided/dmeanpw]: calculate the arithmetic mean of a double-precision floating-point strided array using pairwise summation. - [`dmeanwd( N, x, strideX )`][@stdlib/stats/strided/dmeanwd]: calculate the arithmetic mean of a double-precision floating-point strided array using Welford's algorithm. - [`dmediansorted( N, x, strideX )`][@stdlib/stats/strided/dmediansorted]: calculate the median value of a sorted double-precision floating-point strided array. @@ -87,7 +89,11 @@ The namespace exports the following: - [`dnanmeanwd( N, x, strideX )`][@stdlib/stats/strided/dnanmeanwd]: calculate the arithmetic mean of a double-precision floating-point strided array, using Welford's algorithm and ignoring `NaN` values. - [`dnanmin( N, x, strideX )`][@stdlib/stats/strided/dnanmin]: calculate the minimum value of a double-precision floating-point strided array, ignoring `NaN` values. - [`dnanminabs( N, x, strideX )`][@stdlib/stats/strided/dnanminabs]: calculate the minimum absolute value of a double-precision floating-point strided array, ignoring `NaN` values. +- [`dnanmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/dnanmskmax]: calculate the maximum value of a double-precision floating-point strided array according to a mask, ignoring `NaN` values. +- [`dnanmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/dnanmskmin]: calculate the minimum value of a double-precision floating-point strided array according to a mask, ignoring `NaN` values. +- [`dnanmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/dnanmskrange]: calculate the range of a double-precision floating-point strided array according to a mask, ignoring `NaN` values. - [`dnanrange( N, x, strideX )`][@stdlib/stats/strided/dnanrange]: calculate the range of a double-precision floating-point strided array, ignoring `NaN` values. +- [`dnanstdev( N, correction, x, strideX )`][@stdlib/stats/strided/dnanstdev]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values. - [`dnanstdevch( N, correction, x, strideX )`][@stdlib/stats/strided/dnanstdevch]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass trial mean algorithm. - [`dnanstdevpn( N, correction, x, strideX )`][@stdlib/stats/strided/dnanstdevpn]: calculate the standard deviation of a double-precision floating-point strided array, ignoring `NaN` values and using a two-pass algorithm. - [`dnanstdevtk( N, correction, x, strideX )`][@stdlib/stats/strided/dnanstdevtk]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm. @@ -100,7 +106,9 @@ The namespace exports the following: - [`dnanvariancewd( N, correction, x, strideX )`][@stdlib/stats/strided/dnanvariancewd]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using Welford's algorithm. - [`dnanvarianceyc( N, correction, x, strideX )`][@stdlib/stats/strided/dnanvarianceyc]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer. - [`drange( N, x, strideX )`][@stdlib/stats/strided/drange]: calculate the range of a double-precision floating-point strided array. +- [`dsem( N, correction, x, strideX )`][@stdlib/stats/strided/dsem]: calculate the standard error of the mean of a double-precision floating-point strided array. - [`dsemch( N, correction, x, strideX )`][@stdlib/stats/strided/dsemch]: calculate the standard error of the mean of a double-precision floating-point strided array using a one-pass trial mean algorithm. +- [`dsempn( N, correction, x, strideX )`][@stdlib/stats/strided/dsempn]: calculate the standard error of the mean of a double-precision floating-point strided array using a two-pass algorithm. - [`dsemtk( N, correction, x, strideX )`][@stdlib/stats/strided/dsemtk]: calculate the standard error of the mean of a double-precision floating-point strided array using a one-pass textbook algorithm. - [`dsemwd( N, correction, x, strideX )`][@stdlib/stats/strided/dsemwd]: calculate the standard error of the mean of a double-precision floating-point strided array using Welford's algorithm. - [`dsemyc( N, correction, x, strideX )`][@stdlib/stats/strided/dsemyc]: calculate the standard error of the mean of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer. @@ -113,6 +121,7 @@ The namespace exports the following: - [`dsnanmeanors( N, x, strideX )`][@stdlib/stats/strided/dsnanmeanors]: 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, strideX )`][@stdlib/stats/strided/dsnanmeanpn]: 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, strideX )`][@stdlib/stats/strided/dsnanmeanwd]: 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, strideX )`][@stdlib/stats/strided/dstdev]: calculate the standard deviation of a double-precision floating-point strided array. - [`dstdevch( N, correction, x, strideX )`][@stdlib/stats/strided/dstdevch]: calculate the standard deviation of a double-precision floating-point strided array using a one-pass trial mean algorithm. - [`dstdevpn( N, correction, x, strideX )`][@stdlib/stats/strided/dstdevpn]: calculate the standard deviation of a double-precision floating-point strided array using a two-pass algorithm. - [`dstdevtk( N, correction, x, strideX )`][@stdlib/stats/strided/dstdevtk]: calculate the standard deviation of a double-precision floating-point strided array using a one-pass textbook algorithm. @@ -155,6 +164,9 @@ The namespace exports the following: - [`snanmeanwd( N, x, strideX )`][@stdlib/stats/strided/snanmeanwd]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using Welford's algorithm. - [`snanmin( N, x, strideX )`][@stdlib/stats/strided/snanmin]: calculate the minimum value of a single-precision floating-point strided array, ignoring `NaN` values. - [`snanminabs( N, x, strideX )`][@stdlib/stats/strided/snanminabs]: calculate the minimum absolute value of a single-precision floating-point strided array, ignoring `NaN` values. +- [`snanmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/snanmskmax]: calculate the maximum value of a single-precision floating-point strided array according to a mask, ignoring `NaN` values. +- [`snanmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/snanmskmin]: calculate the minimum value of a single-precision floating-point strided array according to a mask, ignoring `NaN` values. +- [`snanmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/snanmskrange]: calculate the range of a single-precision floating-point strided array according to a mask, ignoring `NaN` values. - [`snanrange( N, x, strideX )`][@stdlib/stats/strided/snanrange]: calculate the range of a single-precision floating-point strided array, ignoring `NaN` values. - [`srange( N, x, strideX )`][@stdlib/stats/strided/srange]: calculate the range of a single-precision floating-point strided array. - [`sstdevch( N, correction, x, strideX )`][@stdlib/stats/strided/sstdevch]: calculate the standard deviation of a single-precision floating-point strided array using a one-pass trial mean algorithm. @@ -233,6 +245,8 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/strided/dmaxsorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmaxsorted +[@stdlib/stats/strided/dmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmean + [@stdlib/stats/strided/dmeankbn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmeankbn [@stdlib/stats/strided/dmeankbn2]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmeankbn2 @@ -243,6 +257,8 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/strided/dmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmeanors +[@stdlib/stats/strided/dmeanpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmeanpn + [@stdlib/stats/strided/dmeanpw]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmeanpw [@stdlib/stats/strided/dmeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmeanwd @@ -281,8 +297,16 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/strided/dnanminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanminabs +[@stdlib/stats/strided/dnanmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmskmax + +[@stdlib/stats/strided/dnanmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmskmin + +[@stdlib/stats/strided/dnanmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmskrange + [@stdlib/stats/strided/dnanrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanrange +[@stdlib/stats/strided/dnanstdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanstdev + [@stdlib/stats/strided/dnanstdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanstdevch [@stdlib/stats/strided/dnanstdevpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanstdevpn @@ -307,8 +331,12 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/strided/drange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/drange +[@stdlib/stats/strided/dsem]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsem + [@stdlib/stats/strided/dsemch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsemch +[@stdlib/stats/strided/dsempn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsempn + [@stdlib/stats/strided/dsemtk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsemtk [@stdlib/stats/strided/dsemwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsemwd @@ -333,6 +361,8 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/strided/dsnanmeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsnanmeanwd +[@stdlib/stats/strided/dstdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dstdev + [@stdlib/stats/strided/dstdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dstdevch [@stdlib/stats/strided/dstdevpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dstdevpn @@ -417,6 +447,12 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/strided/snanminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanminabs +[@stdlib/stats/strided/snanmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmskmax + +[@stdlib/stats/strided/snanmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmskmin + +[@stdlib/stats/strided/snanmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmskrange + [@stdlib/stats/strided/snanrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanrange [@stdlib/stats/strided/srange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/srange