diff --git a/lib/node_modules/@stdlib/stats/base/README.md b/lib/node_modules/@stdlib/stats/base/README.md index 659c3487efab..734bccb0855c 100644 --- a/lib/node_modules/@stdlib/stats/base/README.md +++ b/lib/node_modules/@stdlib/stats/base/README.md @@ -71,58 +71,16 @@ The namespace contains the following statistical functions: - [`dmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dmskmax]: calculate the maximum value of a double-precision floating-point strided array according to a mask. - [`dmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dmskmin]: calculate the minimum value of a double-precision floating-point strided array according to a mask. - [`dmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dmskrange]: calculate the range of a double-precision floating-point strided array according to a mask. -- [`dnanmeanpw( N, x, strideX )`][@stdlib/stats/strided/dnanmeanpw]: calculate the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using pairwise summation. -- [`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. -- [`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/base/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/base/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/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/strided/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, 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. -- [`dnanstdevwd( N, correction, x, strideX )`][@stdlib/stats/strided/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/strided/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, strideX )`][@stdlib/stats/strided/dnanvariance]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values. -- [`dnanvariancech( N, correction, x, strideX )`][@stdlib/stats/strided/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/strided/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/strided/dnanvariancetk]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm. -- [`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, stride )`][@stdlib/stats/base/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, stride )`][@stdlib/stats/base/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. -- [`dsmean( N, x, strideX )`][@stdlib/stats/strided/dsmean]: calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation and returning an extended precision result. - [`dsmeanors( N, x, strideX )`][@stdlib/stats/base/dsmeanors]: calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation with extended accumulation and returning an extended precision result. -- [`dsmeanpn( N, x, strideX )`][@stdlib/stats/strided/dsmeanpn]: calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm with extended accumulation and returning an extended precision result. -- [`dsmeanpw( N, x, strideX )`][@stdlib/stats/strided/dsmeanpw]: calculate the arithmetic mean of a single-precision floating-point strided array using pairwise summation with extended accumulation and returning an extended precision result. -- [`dsmeanwd( N, x, strideX )`][@stdlib/stats/strided/dsmeanwd]: calculate the arithmetic mean of a single-precision floating-point strided array using Welford's algorithm with extended accumulation and returning an extended precision result. -- [`dsnanmean( N, x, strideX )`][@stdlib/stats/strided/dsnanmean]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values, using extended accumulation, and returning an extended precision result. -- [`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, stride )`][@stdlib/stats/base/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. -- [`dstdevwd( N, correction, x, strideX )`][@stdlib/stats/strided/dstdevwd]: calculate the standard deviation of a double-precision floating-point strided array using Welford's algorithm. -- [`dstdevyc( N, correction, x, strideX )`][@stdlib/stats/strided/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, strideX )`][@stdlib/stats/strided/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, strideX )`][@stdlib/stats/strided/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/strided/dvariance]: calculate the variance of a double-precision floating-point strided array. -- [`dvariancech( N, correction, x, strideX )`][@stdlib/stats/strided/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/strided/dvariancepn]: calculate the variance of a double-precision floating-point strided array using a two-pass algorithm. -- [`dvariancetk( N, correction, x, strideX )`][@stdlib/stats/strided/dvariancetk]: calculate the variance of a double-precision floating-point strided array using a one-pass textbook algorithm. -- [`dvariancewd( N, correction, x, strideX )`][@stdlib/stats/strided/dvariancewd]: calculate the variance of a double-precision floating-point strided array using Welford's algorithm. -- [`dvarianceyc( N, correction, x, strideX )`][@stdlib/stats/strided/dvarianceyc]: calculate the variance of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer. - [`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. -- [`dvarmtk( N, mean, correction, x, strideX )`][@stdlib/stats/strided/dvarmtk]: calculate the variance of a double-precision floating-point strided array provided a known mean and using a one-pass textbook algorithm. - [`maxBy( N, x, stride, 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. @@ -171,47 +129,19 @@ The namespace contains the following statistical functions: - [`nanvarianceyc( N, correction, x, strideX )`][@stdlib/stats/base/nanvarianceyc]: calculate the variance of a strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer. - [`rangeBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/range-by]: calculate the range of a strided array via a callback function. - [`range( N, x, stride )`][@stdlib/stats/base/range]: calculate the range of a strided array. -- [`scumax( N, x, strideX, y, strideY )`][@stdlib/stats/strided/scumax]: calculate the cumulative maximum of single-precision floating-point strided array elements. -- [`scumaxabs( N, x, strideX, y, strideY )`][@stdlib/stats/strided/scumaxabs]: calculate the cumulative maximum absolute value of single-precision floating-point strided array elements. -- [`scumin( N, x, strideX, y, strideY )`][@stdlib/stats/strided/scumin]: calculate the cumulative minimum of single-precision floating-point strided array elements. -- [`scuminabs( N, x, strideX, y, strideY )`][@stdlib/stats/strided/scuminabs]: calculate the cumulative minimum absolute value of single-precision floating-point strided array elements. -- [`sdsmean( N, x, strideX )`][@stdlib/stats/strided/sdsmean]: calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation. -- [`sdsmeanors( N, x, strideX )`][@stdlib/stats/strided/sdsmeanors]: calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation with extended accumulation. - [`sdsnanmean( N, x, stride )`][@stdlib/stats/base/sdsnanmean]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using extended accumulation. - [`sdsnanmeanors( N, x, stride )`][@stdlib/stats/base/sdsnanmeanors]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation with extended accumulation. -- [`smax( N, x, strideX )`][@stdlib/stats/strided/smax]: calculate the maximum value of a single-precision floating-point strided array. -- [`smaxabs( N, x, strideX )`][@stdlib/stats/strided/smaxabs]: calculate the maximum absolute value of a single-precision floating-point strided array. -- [`smaxabssorted( N, x, strideX )`][@stdlib/stats/strided/smaxabssorted]: calculate the maximum absolute value of a sorted single-precision floating-point strided array. -- [`smaxsorted( N, x, stride )`][@stdlib/stats/strided/smaxsorted]: calculate the maximum value of a sorted single-precision floating-point strided array. - [`smean( N, x, stride )`][@stdlib/stats/base/smean]: calculate the arithmetic mean of a single-precision floating-point strided array. - [`smeankbn( N, x, stride )`][@stdlib/stats/base/smeankbn]: calculate the arithmetic mean of a single-precision floating-point strided array using an improved Kahan–Babuška algorithm. - [`smeankbn2( N, x, stride )`][@stdlib/stats/base/smeankbn2]: calculate the arithmetic mean of a single-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm. -- [`smeanli( N, x, strideX )`][@stdlib/stats/strided/smeanli]: calculate the arithmetic mean of a single-precision floating-point strided array using a one-pass trial mean algorithm. - [`smeanlipw( N, x, stride )`][@stdlib/stats/base/smeanlipw]: calculate the arithmetic mean of a single-precision floating-point strided array using a one-pass trial mean algorithm with pairwise summation. - [`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, stride )`][@stdlib/stats/base/smeanpn]: calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm. -- [`smeanpw( N, x, strideX )`][@stdlib/stats/strided/smeanpw]: calculate the arithmetic mean of a single-precision floating-point strided array using pairwise summation. -- [`smeanwd( N, x, strideX )`][@stdlib/stats/strided/smeanwd]: calculate the arithmetic mean of a single-precision floating-point strided array using Welford's algorithm. -- [`smediansorted( N, x, strideX )`][@stdlib/stats/strided/smediansorted]: calculate the median value of a sorted single-precision floating-point strided array. - [`smidrange( N, x, strideX )`][@stdlib/stats/base/smidrange]: calculate the mid-range of a single-precision floating-point strided array. -- [`smin( N, x, strideX )`][@stdlib/stats/strided/smin]: calculate the minimum value of a single-precision floating-point strided array. -- [`sminabs( N, x, strideX )`][@stdlib/stats/strided/sminabs]: calculate the minimum absolute value of a single-precision floating-point strided array. -- [`sminsorted( N, x, strideX )`][@stdlib/stats/strided/sminsorted]: calculate the minimum value of a sorted single-precision floating-point strided array. -- [`smskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/smskmax]: calculate the maximum value of a single-precision floating-point strided array according to a mask. -- [`smskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/smskmin]: calculate the minimum value of a single-precision floating-point strided array according to a mask. -- [`smskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/smskrange]: calculate the range of a single-precision floating-point strided array according to a mask. -- [`snanmax( N, x, strideX )`][@stdlib/stats/strided/snanmax]: calculate the maximum value of a single-precision floating-point strided array, ignoring `NaN` values. -- [`snanmaxabs( N, x, strideX )`][@stdlib/stats/strided/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/strided/snanmeanors]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation. -- [`snanmeanpn( N, x, strideX )`][@stdlib/stats/strided/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/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/base/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/base/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/base/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. - [`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. @@ -224,13 +154,8 @@ The namespace contains the following statistical functions: - [`snanvariancetk( N, correction, x, stride )`][@stdlib/stats/base/snanvariancetk]: calculate the variance of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm. - [`snanvariancewd( N, correction, x, stride )`][@stdlib/stats/base/snanvariancewd]: calculate the variance of a single-precision floating-point strided array ignoring `NaN` values and using Welford's algorithm. - [`snanvarianceyc( N, correction, x, stride )`][@stdlib/stats/base/snanvarianceyc]: calculate the variance of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer. -- [`srange( N, x, strideX )`][@stdlib/stats/strided/srange]: calculate the range of a single-precision floating-point strided array. - [`sstdev( N, correction, x, stride )`][@stdlib/stats/base/sstdev]: calculate the standard deviation 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. -- [`sstdevpn( N, correction, x, strideX )`][@stdlib/stats/strided/sstdevpn]: calculate the standard deviation of a single-precision floating-point strided array using a two-pass algorithm. -- [`sstdevtk( N, correction, x, strideX )`][@stdlib/stats/strided/sstdevtk]: calculate the standard deviation of a single-precision floating-point strided array using a one-pass textbook algorithm. - [`sstdevwd( N, correction, x, stride )`][@stdlib/stats/base/sstdevwd]: calculate the standard deviation of a single-precision floating-point strided array using Welford's algorithm. -- [`sstdevyc( N, correction, x, strideX )`][@stdlib/stats/strided/sstdevyc]: calculate the standard deviation of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer. - [`stdev( N, correction, x, stride )`][@stdlib/stats/base/stdev]: calculate the standard deviation of a strided array. - [`stdevch( N, correction, x, stride )`][@stdlib/stats/base/stdevch]: calculate the standard deviation of a strided array using a one-pass trial mean algorithm. - [`stdevpn( N, correction, x, stride )`][@stdlib/stats/base/stdevpn]: calculate the standard deviation of a strided array using a two-pass algorithm. @@ -238,7 +163,6 @@ The namespace contains the following statistical functions: - [`stdevwd( N, correction, x, stride )`][@stdlib/stats/base/stdevwd]: calculate the standard deviation of a strided array using Welford's algorithm. - [`stdevyc( N, correction, x, stride )`][@stdlib/stats/base/stdevyc]: calculate the standard deviation of a strided array using a one-pass algorithm proposed by Youngs and Cramer. - [`svariance( N, correction, x, stride )`][@stdlib/stats/base/svariance]: calculate the variance of a single-precision floating-point strided array. -- [`svariancech( N, correction, x, strideX )`][@stdlib/stats/strided/svariancech]: calculate the variance of a single-precision floating-point strided array using a one-pass trial mean algorithm. - [`svariancepn( N, correction, x, strideX )`][@stdlib/stats/base/svariancepn]: calculate the variance of a single-precision floating-point strided array using a two-pass algorithm. - [`svariancetk( N, correction, x, strideX )`][@stdlib/stats/base/svariancetk]: calculate the variance of a single-precision floating-point strided array using a one-pass textbook algorithm. - [`svariancewd( N, correction, x, stride )`][@stdlib/stats/base/svariancewd]: calculate the variance of a single-precision floating-point strided array using Welford's algorithm. @@ -327,110 +251,26 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/dmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/dmskrange -[@stdlib/stats/strided/dnanmeanpw]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmeanpw - -[@stdlib/stats/strided/dnanmeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmeanwd - -[@stdlib/stats/strided/dnanminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanminabs - [@stdlib/stats/base/dnanmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/dnanmskmax [@stdlib/stats/base/dnanmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/dnanmskmin [@stdlib/stats/base/dnanmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/dnanmskrange -[@stdlib/stats/strided/dnanrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanrange - [@stdlib/stats/base/dnanstdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/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 - -[@stdlib/stats/strided/dnanstdevtk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanstdevtk - -[@stdlib/stats/strided/dnanstdevwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanstdevwd - -[@stdlib/stats/strided/dnanstdevyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanstdevyc - -[@stdlib/stats/strided/dnanvariance]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanvariance - -[@stdlib/stats/strided/dnanvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanvariancech - -[@stdlib/stats/strided/dnanvariancepn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanvariancepn - -[@stdlib/stats/strided/dnanvariancetk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanvariancetk - -[@stdlib/stats/strided/dnanvariancewd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanvariancewd - -[@stdlib/stats/strided/dnanvarianceyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanvarianceyc - -[@stdlib/stats/strided/drange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/drange - [@stdlib/stats/base/dsem]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/dsem -[@stdlib/stats/strided/dsemch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsemch - [@stdlib/stats/base/dsempn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/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 - -[@stdlib/stats/strided/dsemyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsemyc - -[@stdlib/stats/strided/dsmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsmean - [@stdlib/stats/base/dsmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/dsmeanors -[@stdlib/stats/strided/dsmeanpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsmeanpn - -[@stdlib/stats/strided/dsmeanpw]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsmeanpw - -[@stdlib/stats/strided/dsmeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsmeanwd - -[@stdlib/stats/strided/dsnanmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsnanmean - -[@stdlib/stats/strided/dsnanmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsnanmeanors - -[@stdlib/stats/strided/dsnanmeanpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsnanmeanpn - -[@stdlib/stats/strided/dsnanmeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsnanmeanwd - [@stdlib/stats/base/dstdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/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 - -[@stdlib/stats/strided/dstdevtk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dstdevtk - -[@stdlib/stats/strided/dstdevwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dstdevwd - -[@stdlib/stats/strided/dstdevyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dstdevyc - -[@stdlib/stats/strided/dsvariance]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsvariance - -[@stdlib/stats/strided/dsvariancepn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsvariancepn - -[@stdlib/stats/strided/dvariance]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dvariance - -[@stdlib/stats/strided/dvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dvariancech - -[@stdlib/stats/strided/dvariancepn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dvariancepn - -[@stdlib/stats/strided/dvariancetk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dvariancetk - -[@stdlib/stats/strided/dvariancewd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dvariancewd - -[@stdlib/stats/strided/dvarianceyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dvarianceyc - [@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 -[@stdlib/stats/strided/dvarmtk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dvarmtk - [@stdlib/stats/base/max-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/max-by [@stdlib/stats/base/max]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/max @@ -527,88 +367,32 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/range]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/range -[@stdlib/stats/strided/scumax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/scumax - -[@stdlib/stats/strided/scumaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/scumaxabs - -[@stdlib/stats/strided/scumin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/scumin - -[@stdlib/stats/strided/scuminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/scuminabs - -[@stdlib/stats/strided/sdsmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/sdsmean - -[@stdlib/stats/strided/sdsmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/sdsmeanors - [@stdlib/stats/base/sdsnanmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/sdsnanmean [@stdlib/stats/base/sdsnanmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/sdsnanmeanors -[@stdlib/stats/strided/smax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smax - -[@stdlib/stats/strided/smaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smaxabs - -[@stdlib/stats/strided/smaxabssorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smaxabssorted - -[@stdlib/stats/strided/smaxsorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smaxsorted - [@stdlib/stats/base/smean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/smean [@stdlib/stats/base/smeankbn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/smeankbn [@stdlib/stats/base/smeankbn2]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/smeankbn2 -[@stdlib/stats/strided/smeanli]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smeanli - [@stdlib/stats/base/smeanlipw]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/smeanlipw [@stdlib/stats/base/smeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/smeanors [@stdlib/stats/base/smeanpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/smeanpn -[@stdlib/stats/strided/smeanpw]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smeanpw - -[@stdlib/stats/strided/smeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smeanwd - -[@stdlib/stats/strided/smediansorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smediansorted - [@stdlib/stats/base/smidrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/smidrange -[@stdlib/stats/strided/smin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smin - -[@stdlib/stats/strided/sminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/sminabs - -[@stdlib/stats/strided/sminsorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/sminsorted - -[@stdlib/stats/strided/smskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smskmax - -[@stdlib/stats/strided/smskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smskmin - -[@stdlib/stats/strided/smskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smskrange - -[@stdlib/stats/strided/snanmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmax - -[@stdlib/stats/strided/snanmaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmaxabs - [@stdlib/stats/base/snanmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/snanmean -[@stdlib/stats/strided/snanmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmeanors - -[@stdlib/stats/strided/snanmeanpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmeanpn - -[@stdlib/stats/strided/snanmeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmeanwd - -[@stdlib/stats/strided/snanmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanmin - -[@stdlib/stats/strided/snanminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanminabs - [@stdlib/stats/base/snanmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/snanmskmax [@stdlib/stats/base/snanmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/snanmskmin [@stdlib/stats/base/snanmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/snanmskrange -[@stdlib/stats/strided/snanrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/snanrange - [@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 @@ -633,20 +417,10 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/snanvarianceyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/snanvarianceyc -[@stdlib/stats/strided/srange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/srange - [@stdlib/stats/base/sstdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/sstdev -[@stdlib/stats/strided/sstdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/sstdevch - -[@stdlib/stats/strided/sstdevpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/sstdevpn - -[@stdlib/stats/strided/sstdevtk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/sstdevtk - [@stdlib/stats/base/sstdevwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/sstdevwd -[@stdlib/stats/strided/sstdevyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/sstdevyc - [@stdlib/stats/base/stdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/stdev [@stdlib/stats/base/stdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/stdevch @@ -661,8 +435,6 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/svariance]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/svariance -[@stdlib/stats/strided/svariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/svariancech - [@stdlib/stats/base/svariancepn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/svariancepn [@stdlib/stats/base/svariancetk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/svariancetk