diff --git a/lib/node_modules/@stdlib/blas/ext/base/README.md b/lib/node_modules/@stdlib/blas/ext/base/README.md index 33fb49b0104c..74e4363f726c 100644 --- a/lib/node_modules/@stdlib/blas/ext/base/README.md +++ b/lib/node_modules/@stdlib/blas/ext/base/README.md @@ -189,6 +189,7 @@ var o = ns; - [`snansumpw( N, x, strideX )`][@stdlib/blas/ext/base/snansumpw]: calculate the sum of single-precision floating-point strided array elements, ignoring `NaN` values and using pairwise summation. - [`soneTo( N, x, strideX )`][@stdlib/blas/ext/base/sone-to]: fill a single-precision floating-point strided array with linearly spaced numeric elements which increment by `1` starting from one. - [`srev( N, x, strideX )`][@stdlib/blas/ext/base/srev]: reverse a single-precision floating-point strided array in-place. +- [`ssort( N, order, x, strideX )`][@stdlib/blas/ext/base/ssort]: sort a single-precision floating-point strided array. - [`ssort2hp( N, order, x, strideX, y, strideY )`][@stdlib/blas/ext/base/ssort2hp]: simultaneously sort two single-precision floating-point strided arrays based on the sort order of the first array using heapsort. - [`ssort2ins( N, order, x, strideX, y, strideY )`][@stdlib/blas/ext/base/ssort2ins]: simultaneously sort two single-precision floating-point strided arrays based on the sort order of the first array using insertion sort. - [`ssort2sh( N, order, x, strideX, y, strideY )`][@stdlib/blas/ext/base/ssort2sh]: simultaneously sort two single-precision floating-point strided arrays based on the sort order of the first array using Shellsort. @@ -550,6 +551,8 @@ console.log( objectKeys( ns ) ); [@stdlib/blas/ext/base/srev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/ext/base/srev +[@stdlib/blas/ext/base/ssort]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/ext/base/ssort + [@stdlib/blas/ext/base/ssort2hp]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/ext/base/ssort2hp [@stdlib/blas/ext/base/ssort2ins]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/ext/base/ssort2ins 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 209e2187d6f5..9421c5ef11e5 100644 --- a/lib/node_modules/@stdlib/blas/ext/base/ndarray/README.md +++ b/lib/node_modules/@stdlib/blas/ext/base/ndarray/README.md @@ -64,6 +64,7 @@ The namespace exposes the following APIs: - [`dnansumors( arrays )`][@stdlib/blas/ext/base/ndarray/dnansumors]: compute the sum of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using ordinary recursive summation. - [`dnansumpw( arrays )`][@stdlib/blas/ext/base/ndarray/dnansumpw]: compute the sum of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using pairwise summation. - [`doneTo( arrays )`][@stdlib/blas/ext/base/ndarray/done-to]: fill a one-dimensional double-precision floating-point ndarray with linearly spaced numeric elements which increment by `1` starting from one. +- [`dsort( arrays )`][@stdlib/blas/ext/base/ndarray/dsort]: sort a one-dimensional double-precision floating-point ndarray. - [`dsorthp( arrays )`][@stdlib/blas/ext/base/ndarray/dsorthp]: sort a one-dimensional double-precision floating-point ndarray using heapsort. - [`dsortins( arrays )`][@stdlib/blas/ext/base/ndarray/dsortins]: sort a one-dimensional double-precision floating-point ndarray using insertion sort. - [`dsortsh( arrays )`][@stdlib/blas/ext/base/ndarray/dsortsh]: sort a one-dimensional double-precision floating-point ndarray using Shellsort. @@ -114,6 +115,7 @@ The namespace exposes the following APIs: - [`snansumors( arrays )`][@stdlib/blas/ext/base/ndarray/snansumors]: compute the sum of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values and using ordinary recursive summation. - [`snansumpw( arrays )`][@stdlib/blas/ext/base/ndarray/snansumpw]: compute the sum of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values and using pairwise summation. - [`soneTo( arrays )`][@stdlib/blas/ext/base/ndarray/sone-to]: fill a one-dimensional single-precision floating-point ndarray with linearly spaced numeric elements which increment by `1` starting from one. +- [`ssort( arrays )`][@stdlib/blas/ext/base/ndarray/ssort]: sort a one-dimensional single-precision floating-point ndarray. - [`ssorthp( arrays )`][@stdlib/blas/ext/base/ndarray/ssorthp]: sort a one-dimensional single-precision floating-point ndarray using heapsort. - [`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. @@ -205,6 +207,8 @@ console.log( objectKeys( ns ) ); [@stdlib/blas/ext/base/ndarray/done-to]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/ext/base/ndarray/done-to +[@stdlib/blas/ext/base/ndarray/dsort]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/ext/base/ndarray/dsort + [@stdlib/blas/ext/base/ndarray/dsorthp]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/ext/base/ndarray/dsorthp [@stdlib/blas/ext/base/ndarray/dsortins]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/ext/base/ndarray/dsortins @@ -305,6 +309,8 @@ console.log( objectKeys( ns ) ); [@stdlib/blas/ext/base/ndarray/sone-to]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/ext/base/ndarray/sone-to +[@stdlib/blas/ext/base/ndarray/ssort]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/ext/base/ndarray/ssort + [@stdlib/blas/ext/base/ndarray/ssorthp]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/ext/base/ndarray/ssorthp [@stdlib/blas/ext/base/ndarray/ssum]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/ext/base/ndarray/ssum diff --git a/lib/node_modules/@stdlib/math/base/tools/README.md b/lib/node_modules/@stdlib/math/base/tools/README.md index 956569a9d178..064389dccaf4 100644 --- a/lib/node_modules/@stdlib/math/base/tools/README.md +++ b/lib/node_modules/@stdlib/math/base/tools/README.md @@ -43,6 +43,7 @@ var o = tools;
+- [`chebyshevSeries( x, c )`][@stdlib/math/base/tools/chebyshev-series]: evaluate a Chebyshev series using double-precision floating-point arithmetic. - [`continuedFraction( generator[, options ] )`][@stdlib/math/base/tools/continued-fraction]: continued fraction approximation. - [`evalpoly( c, x )`][@stdlib/math/base/tools/evalpoly]: evaluate a polynomial using double-precision floating-point arithmetic. - [`evalpolyf( c, x )`][@stdlib/math/base/tools/evalpolyf]: evaluate a polynomial using single-precision floating-point arithmetic. @@ -96,6 +97,8 @@ console.log( objectKeys( tools ) ); +[@stdlib/math/base/tools/chebyshev-series]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/tools/chebyshev-series + [@stdlib/math/base/tools/continued-fraction]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/tools/continued-fraction [@stdlib/math/base/tools/evalpoly]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/tools/evalpoly diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/README.md b/lib/node_modules/@stdlib/stats/base/ndarray/README.md index 8723d51770f2..9b8a87087fc6 100644 --- a/lib/node_modules/@stdlib/stats/base/ndarray/README.md +++ b/lib/node_modules/@stdlib/stats/base/ndarray/README.md @@ -73,6 +73,7 @@ The namespace exposes the following APIs: - [`dminabs( arrays )`][@stdlib/stats/base/ndarray/dminabs]: compute the minimum absolute value of a one-dimensional double-precision floating-point ndarray. - [`dminsorted( arrays )`][@stdlib/stats/base/ndarray/dminsorted]: compute the minimum value of a sorted one-dimensional double-precision floating-point ndarray. - [`dmskmax( arrays )`][@stdlib/stats/base/ndarray/dmskmax]: calculate the maximum value of a one-dimensional double-precision floating-point ndarray according to a mask. +- [`dmskmaxabs( arrays )`][@stdlib/stats/base/ndarray/dmskmaxabs]: calculate the maximum absolute value of a one-dimensional double-precision floating-point ndarray according to a mask. - [`dmskmin( arrays )`][@stdlib/stats/base/ndarray/dmskmin]: calculate the minimum value of a one-dimensional double-precision floating-point ndarray according to a mask. - [`dmskrange( arrays )`][@stdlib/stats/base/ndarray/dmskrange]: calculate the range of a one-dimensional double-precision floating-point ndarray according to a mask. - [`dnanmax( arrays )`][@stdlib/stats/base/ndarray/dnanmax]: compute the maximum value of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. @@ -86,9 +87,14 @@ The namespace exposes the following APIs: - [`dnanmin( arrays )`][@stdlib/stats/base/ndarray/dnanmin]: compute the minimum value of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. - [`dnanminabs( arrays )`][@stdlib/stats/base/ndarray/dnanminabs]: compute the minimum absolute value of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. - [`dnanmskmax( arrays )`][@stdlib/stats/base/ndarray/dnanmskmax]: compute the maximum value of a double-precision floating-point ndarray according to a mask, ignoring `NaN` values. +- [`dnanmskmaxabs( arrays )`][@stdlib/stats/base/ndarray/dnanmskmaxabs]: compute the maximum absolute value of a double-precision floating-point ndarray according to a mask, ignoring `NaN` values. - [`dnanmskmin( arrays )`][@stdlib/stats/base/ndarray/dnanmskmin]: compute the minimum value of a double-precision floating-point ndarray according to a mask, ignoring `NaN` values. +- [`dnanmskminabs( arrays )`][@stdlib/stats/base/ndarray/dnanmskminabs]: compute the minimum absolute value of a double-precision floating-point ndarray according to a mask, ignoring `NaN` values. - [`dnanmskrange( arrays )`][@stdlib/stats/base/ndarray/dnanmskrange]: calculate the range of a one-dimensional double-precision floating-point ndarray according to a mask, ignoring `NaN` values. - [`dnanrange( arrays )`][@stdlib/stats/base/ndarray/dnanrange]: compute the range of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. +- [`dnanstdev( arrays )`][@stdlib/stats/base/ndarray/dnanstdev]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. +- [`dnanstdevch( arrays )`][@stdlib/stats/base/ndarray/dnanstdevch]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using a one-pass trial mean algorithm. +- [`dnanstdevpn( arrays )`][@stdlib/stats/base/ndarray/dnanstdevpn]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using a two-pass algorithm. - [`drange( arrays )`][@stdlib/stats/base/ndarray/drange]: compute the range of a one-dimensional double-precision floating-point ndarray. - [`drangeabs( arrays )`][@stdlib/stats/base/ndarray/drangeabs]: compute the range of absolute values of a one-dimensional double-precision floating-point ndarray. - [`dstdev( arrays )`][@stdlib/stats/base/ndarray/dstdev]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray. @@ -135,6 +141,7 @@ The namespace exposes the following APIs: - [`nanmin( arrays )`][@stdlib/stats/base/ndarray/nanmin]: compute the minimum value of a one-dimensional ndarray, ignoring `NaN` values. - [`nanminabs( arrays )`][@stdlib/stats/base/ndarray/nanminabs]: compute the minimum absolute value of a one-dimensional ndarray, ignoring `NaN` values. - [`nanmskmax( arrays )`][@stdlib/stats/base/ndarray/nanmskmax]: calculate the maximum value of a one-dimensional ndarray according to a mask, ignoring `NaN` values. +- [`nanmskmidrange( arrays )`][@stdlib/stats/base/ndarray/nanmskmidrange]: calculate the mid-range of a one-dimensional ndarray according to a mask, ignoring `NaN` values. - [`nanmskmin( arrays )`][@stdlib/stats/base/ndarray/nanmskmin]: calculate the minimum value of a one-dimensional ndarray according to a mask, ignoring `NaN` values. - [`nanmskrange( arrays )`][@stdlib/stats/base/ndarray/nanmskrange]: calculate the range of a one-dimensional ndarray according to a mask, ignoring `NaN` values. - [`nanrangeBy( arrays, clbk[, thisArg ] )`][@stdlib/stats/base/ndarray/nanrange-by]: calculate the range of a one-dimensional ndarray via a callback function, ignoring `NaN` values. @@ -183,7 +190,10 @@ The namespace exposes the following APIs: - [`snanmin( arrays )`][@stdlib/stats/base/ndarray/snanmin]: compute the minimum value of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values. - [`snanminabs( arrays )`][@stdlib/stats/base/ndarray/snanminabs]: compute the minimum absolute value of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values. - [`snanmskmax( arrays )`][@stdlib/stats/base/ndarray/snanmskmax]: calculate the maximum value of a one-dimensional single-precision floating-point ndarray according to a mask, ignoring `NaN` values. +- [`snanmskmaxabs( arrays )`][@stdlib/stats/base/ndarray/snanmskmaxabs]: compute the maximum absolute value of a one-dimensional single-precision floating-point ndarray according to a mask, ignoring `NaN` values. +- [`snanmskmidrange( arrays )`][@stdlib/stats/base/ndarray/snanmskmidrange]: calculate the mid-range of a one-dimensional single-precision floating-point ndarray according to a mask, ignoring `NaN` values. - [`snanmskmin( arrays )`][@stdlib/stats/base/ndarray/snanmskmin]: calculate the minimum value of a one-dimensional single-precision floating-point ndarray according to a mask, ignoring `NaN` values. +- [`snanmskminabs( arrays )`][@stdlib/stats/base/ndarray/snanmskminabs]: compute the minimum absolute value of a one-dimensional single-precision floating-point ndarray according to a mask, ignoring `NaN` values. - [`snanmskrange( arrays )`][@stdlib/stats/base/ndarray/snanmskrange]: calculate the range of a one-dimensional single-precision floating-point ndarray according to a mask, ignoring `NaN` values. - [`snanrange( arrays )`][@stdlib/stats/base/ndarray/snanrange]: compute the range of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values. - [`srange( arrays )`][@stdlib/stats/base/ndarray/srange]: compute the range of a one-dimensional single-precision floating-point ndarray. @@ -204,7 +214,10 @@ The namespace exposes the following APIs: - [`sztest2( arrays )`][@stdlib/stats/base/ndarray/sztest2]: compute a two-sample Z-test for two one-dimensional single-precision floating-point ndarrays. - [`variance( arrays )`][@stdlib/stats/base/ndarray/variance]: calculate the variance of a one-dimensional ndarray. - [`variancech( arrays )`][@stdlib/stats/base/ndarray/variancech]: calculate the variance of a one-dimensional ndarray using a one-pass trial mean algorithm. +- [`variancepn( arrays )`][@stdlib/stats/base/ndarray/variancepn]: calculate the variance of a one-dimensional ndarray using a two-pass algorithm. +- [`variancetk( arrays )`][@stdlib/stats/base/ndarray/variancetk]: calculate the variance of a one-dimensional ndarray using a one-pass textbook algorithm. - [`variancewd( arrays )`][@stdlib/stats/base/ndarray/variancewd]: calculate the variance of a one-dimensional ndarray using Welford's algorithm. +- [`varianceyc( arrays )`][@stdlib/stats/base/ndarray/varianceyc]: calculate the variance of a one-dimensional ndarray using a one-pass algorithm proposed by Youngs and Cramer. - [`ztest( arrays )`][@stdlib/stats/base/ndarray/ztest]: compute a one-sample Z-test for a one-dimensional ndarray. - [`ztest2( arrays )`][@stdlib/stats/base/ndarray/ztest2]: compute a two-sample Z-test for two one-dimensional ndarrays. @@ -305,6 +318,8 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/ndarray/dmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmskmax +[@stdlib/stats/base/ndarray/dmskmaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmskmaxabs + [@stdlib/stats/base/ndarray/dmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmskmin [@stdlib/stats/base/ndarray/dmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmskrange @@ -331,12 +346,22 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/ndarray/dnanmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmskmax +[@stdlib/stats/base/ndarray/dnanmskmaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmskmaxabs + [@stdlib/stats/base/ndarray/dnanmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmskmin +[@stdlib/stats/base/ndarray/dnanmskminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmskminabs + [@stdlib/stats/base/ndarray/dnanmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmskrange [@stdlib/stats/base/ndarray/dnanrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanrange +[@stdlib/stats/base/ndarray/dnanstdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanstdev + +[@stdlib/stats/base/ndarray/dnanstdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanstdevch + +[@stdlib/stats/base/ndarray/dnanstdevpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanstdevpn + [@stdlib/stats/base/ndarray/drange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/drange [@stdlib/stats/base/ndarray/drangeabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/drangeabs @@ -429,6 +454,8 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/ndarray/nanmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmskmax +[@stdlib/stats/base/ndarray/nanmskmidrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmskmidrange + [@stdlib/stats/base/ndarray/nanmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmskmin [@stdlib/stats/base/ndarray/nanmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmskrange @@ -525,8 +552,14 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/ndarray/snanmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmskmax +[@stdlib/stats/base/ndarray/snanmskmaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmskmaxabs + +[@stdlib/stats/base/ndarray/snanmskmidrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmskmidrange + [@stdlib/stats/base/ndarray/snanmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmskmin +[@stdlib/stats/base/ndarray/snanmskminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmskminabs + [@stdlib/stats/base/ndarray/snanmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmskrange [@stdlib/stats/base/ndarray/snanrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanrange @@ -567,8 +600,14 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/ndarray/variancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/variancech +[@stdlib/stats/base/ndarray/variancepn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/variancepn + +[@stdlib/stats/base/ndarray/variancetk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/variancetk + [@stdlib/stats/base/ndarray/variancewd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/variancewd +[@stdlib/stats/base/ndarray/varianceyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/varianceyc + [@stdlib/stats/base/ndarray/ztest]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/ztest [@stdlib/stats/base/ndarray/ztest2]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/ztest2