diff --git a/lib/node_modules/@stdlib/math/base/ops/README.md b/lib/node_modules/@stdlib/math/base/ops/README.md index e42225a54a75..df3f5af5e2a1 100644 --- a/lib/node_modules/@stdlib/math/base/ops/README.md +++ b/lib/node_modules/@stdlib/math/base/ops/README.md @@ -45,7 +45,6 @@ The namespace contains the following functions:
-- [`cnegf( z )`][@stdlib/complex/float32/base/neg]: negate a single-precision complex floating-point number. - [`csub( z1, z2 )`][@stdlib/math/base/ops/csub]: subtract two double-precision complex floating-point numbers. - [`csubf( z1, z2 )`][@stdlib/math/base/ops/csubf]: subtract two single-precision complex floating-point numbers. @@ -89,8 +88,6 @@ console.log( ns ); -[@stdlib/complex/float32/base/neg]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/complex/float32/base/neg - [@stdlib/math/base/ops/csub]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/ops/csub [@stdlib/math/base/ops/csubf]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/ops/csubf diff --git a/lib/node_modules/@stdlib/stats/base/README.md b/lib/node_modules/@stdlib/stats/base/README.md index 14659ee62370..75209872be3c 100644 --- a/lib/node_modules/@stdlib/stats/base/README.md +++ b/lib/node_modules/@stdlib/stats/base/README.md @@ -81,12 +81,12 @@ The namespace contains the following statistical functions: - [`dnanmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/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/base/dnanrange]: calculate the range of a double-precision floating-point strided array, ignoring `NaN` values. - [`dnanstdev( N, correction, x, stride )`][@stdlib/stats/base/dnanstdev]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values. -- [`dnanstdevch( N, correction, x, stride )`][@stdlib/stats/base/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, stride )`][@stdlib/stats/base/dnanstdevpn]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm. +- [`dnanstdevch( N, correction, x, strideX )`][@stdlib/stats/base/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/base/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/base/dnanstdevtk]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm. - [`dnanstdevwd( N, correction, x, strideX )`][@stdlib/stats/base/dnanstdevwd]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using Welford's algorithm. - [`dnanstdevyc( N, correction, x, strideX )`][@stdlib/stats/base/dnanstdevyc]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer. -- [`dnanvariance( N, correction, x, stride )`][@stdlib/stats/base/dnanvariance]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values. +- [`dnanvariance( N, correction, x, strideX )`][@stdlib/stats/base/dnanvariance]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values. - [`dnanvariancech( N, correction, x, strideX )`][@stdlib/stats/base/dnanvariancech]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass trial mean algorithm. - [`dnanvariancepn( N, correction, x, strideX )`][@stdlib/stats/base/dnanvariancepn]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm. - [`dnanvariancetk( N, correction, x, strideX )`][@stdlib/stats/base/dnanvariancetk]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm. @@ -115,7 +115,7 @@ The namespace contains the following statistical functions: - [`dstdevwd( N, correction, x, strideX )`][@stdlib/stats/base/dstdevwd]: calculate the standard deviation of a double-precision floating-point strided array using Welford's algorithm. - [`dstdevyc( N, correction, x, strideX )`][@stdlib/stats/base/dstdevyc]: calculate the standard deviation of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer. - [`dsvariance( N, correction, x, stride )`][@stdlib/stats/base/dsvariance]: calculate the variance of a single-precision floating-point strided array using extended accumulation and returning an extended precision result. -- [`dsvariancepn( N, correction, x, stride )`][@stdlib/stats/base/dsvariancepn]: calculate the variance of a single-precision floating-point strided array using a two-pass algorithm with extended accumulation and returning an extended precision result. +- [`dsvariancepn( N, correction, x, strideX )`][@stdlib/stats/base/dsvariancepn]: calculate the variance of a single-precision floating-point strided array using a two-pass algorithm with extended accumulation and returning an extended precision result. - [`dvariance( N, correction, x, strideX )`][@stdlib/stats/base/dvariance]: calculate the variance of a double-precision floating-point strided array. - [`dvariancech( N, correction, x, strideX )`][@stdlib/stats/base/dvariancech]: calculate the variance of a double-precision floating-point strided array using a one-pass trial mean algorithm. - [`dvariancepn( N, correction, x, strideX )`][@stdlib/stats/base/dvariancepn]: calculate the variance of a double-precision floating-point strided array using a two-pass algorithm. @@ -206,8 +206,8 @@ The namespace contains the following statistical functions: - [`snanmaxabs( N, x, strideX )`][@stdlib/stats/base/snanmaxabs]: calculate the maximum absolute value of a single-precision floating-point strided array, ignoring `NaN` values. - [`snanmean( N, x, stride )`][@stdlib/stats/base/snanmean]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values. - [`snanmeanors( N, x, strideX )`][@stdlib/stats/base/snanmeanors]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation. -- [`snanmeanpn( N, x, stride )`][@stdlib/stats/base/snanmeanpn]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm. -- [`snanmeanwd( N, x, stride )`][@stdlib/stats/base/snanmeanwd]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using Welford's algorithm. +- [`snanmeanpn( N, x, strideX )`][@stdlib/stats/base/snanmeanpn]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm. +- [`snanmeanwd( N, x, strideX )`][@stdlib/stats/base/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/base/snanmin]: calculate the minimum value of a single-precision floating-point strided array, ignoring `NaN` values. - [`snanminabs( N, x, strideX )`][@stdlib/stats/base/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/base/snanmskmax]: calculate the maximum value of a single-precision floating-point strided array according to a mask, ignoring `NaN` values.