diff --git a/lib/node_modules/@stdlib/blas/base/README.md b/lib/node_modules/@stdlib/blas/base/README.md index 88282f103e5e..4038f46a4ed9 100644 --- a/lib/node_modules/@stdlib/blas/base/README.md +++ b/lib/node_modules/@stdlib/blas/base/README.md @@ -48,7 +48,8 @@ var o = blas; - [`caxpy( N, alpha, x, strideX, y, strideY )`][@stdlib/blas/base/caxpy]: scale a single-precision complex floating-point vector by a single-precision complex floating-point constant and add the result to a single-precision complex floating-point vector. - [`ccopy( N, x, strideX, y, strideY )`][@stdlib/blas/base/ccopy]: copy values from one complex single-precision floating-point vector to another complex single-precision floating-point vector. - [`cscal( N, alpha, x, strideX )`][@stdlib/blas/base/cscal]: scales a single-precision complex floating-point vector by a single-precision complex floating-point constant. -- [`csrot( N, cx, strideX, cy, strideY, c, s )`][@stdlib/blas/base/csrot]: applies a plane rotation. +- [`csrot( N, x, strideX, y, strideY, c, s )`][@stdlib/blas/base/csrot]: apply a plane rotation. +- [`csscal( N, alpha, x, strideX )`][@stdlib/blas/base/csscal]: scale a single-precision complex floating-point vector by a single-precision floating-point constant. - [`cswap( N, x, strideX, y, strideY )`][@stdlib/blas/base/cswap]: interchange two complex single-precision floating-point vectors. - [`dasum( N, x, stride )`][@stdlib/blas/base/dasum]: compute the sum of absolute values (_L1_ norm). - [`daxpy( N, alpha, x, strideX, y, strideY )`][@stdlib/blas/base/daxpy]: multiply a vector `x` by a constant `alpha` and add the result to `y`. @@ -86,7 +87,7 @@ var o = blas; - [`sswap( N, x, strideX, y, strideY )`][@stdlib/blas/base/sswap]: interchange two single-precision floating-point vectors. - [`zaxpy( N, alpha, x, strideX, y, strideY )`][@stdlib/blas/base/zaxpy]: scale a double-precision complex floating-point vector by a double-precision complex floating-point constant and add the result to a double-precision complex floating-point vector. - [`zcopy( N, x, strideX, y, strideY )`][@stdlib/blas/base/zcopy]: copy values from one complex double-precision floating-point vector to another complex double-precision floating-point vector. -- [`zdrot( N, zx, strideX, zy, strideY, c, s )`][@stdlib/blas/base/zdrot]: applies a plane rotation. +- [`zdrot( N, x, strideX, y, strideY, c, s )`][@stdlib/blas/base/zdrot]: apply a plane rotation. - [`zdscal( N, da, zx, strideZX )`][@stdlib/blas/base/zdscal]: scale a double-precision complex floating-point vector by a double-precision floating-point constant. - [`zscal( N, alpha, x, strideX )`][@stdlib/blas/base/zscal]: scales a double-precision complex floating-point vector by a double-precision complex floating-point constant. - [`zswap( N, x, strideX, y, strideY )`][@stdlib/blas/base/zswap]: interchange two complex double-precision floating-point vectors. @@ -328,6 +329,8 @@ console.log( objectKeys( blas ) ); [@stdlib/blas/base/csrot]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/csrot +[@stdlib/blas/base/csscal]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/csscal + [@stdlib/blas/base/cswap]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/cswap [@stdlib/blas/base/dasum]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/dasum diff --git a/lib/node_modules/@stdlib/complex/float32/base/README.md b/lib/node_modules/@stdlib/complex/float32/base/README.md index 44d19ee7e2c4..8ee85ccca8ea 100644 --- a/lib/node_modules/@stdlib/complex/float32/base/README.md +++ b/lib/node_modules/@stdlib/complex/float32/base/README.md @@ -61,6 +61,7 @@ The namespace contains the following functions: - [`identity( z )`][@stdlib/complex/float32/base/identity]: evaluate the identity function of a single-precision complex floating-point number. - [`mul( z1, z2 )`][@stdlib/complex/float32/base/mul]: multiply two single-precision complex floating-point numbers. - [`neg( z )`][@stdlib/complex/float32/base/neg]: negate a single-precision complex floating-point number. +- [`scale( alpha, c )`][@stdlib/complex/float32/base/scale]: scale a single-precision complex floating-point number by a real-valued single-precision floating-point scalar constant. - [`sub( z1, z2 )`][@stdlib/complex/float32/base/sub]: subtract two single-precision complex floating-point numbers. @@ -120,6 +121,8 @@ console.log( objectKeys( ns ) ); [@stdlib/complex/float32/base/neg]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/complex/float32/base/neg +[@stdlib/complex/float32/base/scale]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/complex/float32/base/scale + [@stdlib/complex/float32/base/sub]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/complex/float32/base/sub [@stdlib/complex/float32/base/assert]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/complex/float32/base/assert diff --git a/lib/node_modules/@stdlib/math/base/special/README.md b/lib/node_modules/@stdlib/math/base/special/README.md index b801d5119ddd..bbb405a62585 100644 --- a/lib/node_modules/@stdlib/math/base/special/README.md +++ b/lib/node_modules/@stdlib/math/base/special/README.md @@ -326,7 +326,7 @@ var fcns = special; - [`sqrtpi( x )`][@stdlib/math/base/special/sqrtpi]: compute the principal square root of the product of π and a positive number. - [`tribonacci( n )`][@stdlib/math/base/special/tribonacci]: compute the nth Tribonacci number. - [`trigamma( x )`][@stdlib/math/base/special/trigamma]: trigamma function. -- [`wrap( v, min, max )`][@stdlib/math/base/special/wrap]: wrap a value on the half-open interval `[min,max)`. +- [`wrap( v, min, max )`][@stdlib/math/base/special/wrap]: wrap a value to the half-open interval `[min,max)`. diff --git a/lib/node_modules/@stdlib/stats/README.md b/lib/node_modules/@stdlib/stats/README.md index 6a24f4538223..0bd25f25676f 100644 --- a/lib/node_modules/@stdlib/stats/README.md +++ b/lib/node_modules/@stdlib/stats/README.md @@ -83,6 +83,13 @@ The namespace further contains functions for computing statistics on arrays as p +
+ +- [`array`][@stdlib/stats/array]: statistical functions for arrays. +- [`strided`][@stdlib/stats/strided]: statistical operations for strided arrays. + +
+ The `base` sub-namespace contains lower-level statistical functions, including a `dists` namespace containing functions related to a wide assortment of probability distributions. @@ -165,6 +172,10 @@ console.log( objectKeys( statistics ) ); [@stdlib/stats/base]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base +[@stdlib/stats/array]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array + +[@stdlib/stats/strided]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided + [@stdlib/stats/incr]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/incr [@stdlib/stats/iter]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/iter diff --git a/lib/node_modules/@stdlib/stats/array/README.md b/lib/node_modules/@stdlib/stats/array/README.md index 721900e13b59..d0ffecf466a1 100644 --- a/lib/node_modules/@stdlib/stats/array/README.md +++ b/lib/node_modules/@stdlib/stats/array/README.md @@ -53,6 +53,27 @@ The namespace exports the following: +
+ +- [`maxBy( x, clbk[, thisArg] )`][@stdlib/stats/array/max-by]: calculate the maximum value of an array via a callback function. +- [`max( x )`][@stdlib/stats/array/max]: calculate the maximum value of an array. +- [`maxabs( x )`][@stdlib/stats/array/maxabs]: calculate the maximum absolute value of an array. +- [`maxsorted( x )`][@stdlib/stats/array/maxsorted]: calculate the maximum value of a sorted array. +- [`mean( x )`][@stdlib/stats/array/mean]: calculate the arithmetic mean of an array. +- [`mediansorted( x )`][@stdlib/stats/array/mediansorted]: calculate the median value of a sorted array. +- [`minBy( x, clbk[, thisArg] )`][@stdlib/stats/array/min-by]: calculate the minimum value of an array via a callback function. +- [`min( x )`][@stdlib/stats/array/min]: calculate the minimum value of an array. +- [`minabs( x )`][@stdlib/stats/array/minabs]: calculate the minimum absolute value of an array. +- [`minsorted( x )`][@stdlib/stats/array/minsorted]: calculate the minimum value of a sorted array. +- [`mskmax( x, mask )`][@stdlib/stats/array/mskmax]: calculate the maximum value of an array according to a mask. +- [`mskmin( x, mask )`][@stdlib/stats/array/mskmin]: calculate the minimum value of an array according to a mask. +- [`mskrange( x, mask )`][@stdlib/stats/array/mskrange]: calculate the range of an array according to a mask. +- [`nanmax( x )`][@stdlib/stats/array/nanmax]: calculate the maximum value of an array, ignoring `NaN` values. +- [`nanmin( x )`][@stdlib/stats/array/nanmin]: calculate the minimum value of an array, ignoring `NaN` values. +- [`varianceyc( x[, correction] )`][@stdlib/stats/array/varianceyc]: calculate the variance of an array using a one-pass algorithm proposed by Youngs and Cramer. + +
+ @@ -100,6 +121,42 @@ console.log( objectKeys( ns ) );
+ + +[@stdlib/stats/array/max-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/max-by + +[@stdlib/stats/array/max]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/max + +[@stdlib/stats/array/maxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/maxabs + +[@stdlib/stats/array/maxsorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/maxsorted + +[@stdlib/stats/array/mean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/mean + +[@stdlib/stats/array/mediansorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/mediansorted + +[@stdlib/stats/array/min-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/min-by + +[@stdlib/stats/array/min]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/min + +[@stdlib/stats/array/minabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/minabs + +[@stdlib/stats/array/minsorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/minsorted + +[@stdlib/stats/array/mskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/mskmax + +[@stdlib/stats/array/mskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/mskmin + +[@stdlib/stats/array/mskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/mskrange + +[@stdlib/stats/array/nanmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/nanmax + +[@stdlib/stats/array/nanmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/array/nanmin + +[@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/strided/README.md b/lib/node_modules/@stdlib/stats/strided/README.md index d2ab48686213..abcbb9da8e8b 100644 --- a/lib/node_modules/@stdlib/stats/strided/README.md +++ b/lib/node_modules/@stdlib/stats/strided/README.md @@ -53,6 +53,121 @@ The namespace exports the following: +
+ +- [`dcumax( N, x, strideX, y, strideY )`][@stdlib/stats/strided/dcumax]: calculate the cumulative maximum of double-precision floating-point strided array elements. +- [`dcumaxabs( N, x, strideX, y, strideY )`][@stdlib/stats/strided/dcumaxabs]: calculate the cumulative maximum absolute value of double-precision floating-point strided array elements. +- [`dcumin( N, x, strideX, y, strideY )`][@stdlib/stats/strided/dcumin]: calculate the cumulative minimum of double-precision floating-point strided array elements. +- [`dcuminabs( N, x, strideX, y, strideY )`][@stdlib/stats/strided/dcuminabs]: calculate the cumulative minimum absolute value of double-precision floating-point strided array elements. +- [`dmax( N, x, strideX )`][@stdlib/stats/strided/dmax]: calculate the maximum value of a double-precision floating-point strided array. +- [`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. +- [`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. +- [`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. +- [`dmidrange( N, x, strideX )`][@stdlib/stats/strided/dmidrange]: calculate the mid-range of a double-precision floating-point strided array. +- [`dmin( N, x, strideX )`][@stdlib/stats/strided/dmin]: calculate the minimum value of a double-precision floating-point strided array. +- [`dminabs( N, x, strideX )`][@stdlib/stats/strided/dminabs]: calculate the minimum absolute value of a double-precision floating-point strided array. +- [`dminsorted( N, x, strideX )`][@stdlib/stats/strided/dminsorted]: calculate the minimum value of a sorted double-precision floating-point strided array. +- [`dmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/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/strided/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/strided/dmskrange]: calculate the range of a double-precision floating-point strided array according to a mask. +- [`dnanmax( N, x, strideX )`][@stdlib/stats/strided/dnanmax]: calculate the maximum value of a double-precision floating-point strided array, ignoring `NaN` values. +- [`dnanmaxabs( N, x, strideX )`][@stdlib/stats/strided/dnanmaxabs]: calculate the maximum absolute value of a double-precision floating-point strided array, ignoring `NaN` values. +- [`dnanmean( N, x, strideX )`][@stdlib/stats/strided/dnanmean]: calculate the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values. +- [`dnanmeanors( N, x, strideX )`][@stdlib/stats/strided/dnanmeanors]: calculate the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation. +- [`dnanmeanpn( N, x, strideX )`][@stdlib/stats/strided/dnanmeanpn]: calculate the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm. +- [`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. +- [`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. +- [`dnanrange( N, x, strideX )`][@stdlib/stats/strided/dnanrange]: calculate the range 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. +- [`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. +- [`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/strided/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. +- [`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. +- [`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. +- [`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. +- [`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. +- [`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. +- [`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/strided/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. +- [`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. +- [`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. +- [`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. +- [`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. +- [`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/strided/svariancepn]: calculate the variance of a single-precision floating-point strided array using a two-pass algorithm. +- [`svariancetk( N, correction, x, strideX )`][@stdlib/stats/strided/svariancetk]: calculate the variance of a single-precision floating-point strided array using a one-pass textbook algorithm. +- [`svarianceyc( N, correction, x, strideX )`][@stdlib/stats/strided/svarianceyc]: calculate the variance of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer. + +
+ @@ -100,6 +215,230 @@ console.log( objectKeys( ns ) );
+ + +[@stdlib/stats/strided/dcumax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dcumax + +[@stdlib/stats/strided/dcumaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dcumaxabs + +[@stdlib/stats/strided/dcumin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dcumin + +[@stdlib/stats/strided/dcuminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dcuminabs + +[@stdlib/stats/strided/dmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmax + +[@stdlib/stats/strided/dmaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmaxabs + +[@stdlib/stats/strided/dmaxabssorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmaxabssorted + +[@stdlib/stats/strided/dmaxsorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmaxsorted + +[@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 + +[@stdlib/stats/strided/dmeanli]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmeanli + +[@stdlib/stats/strided/dmeanlipw]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmeanlipw + +[@stdlib/stats/strided/dmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmeanors + +[@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 + +[@stdlib/stats/strided/dmediansorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmediansorted + +[@stdlib/stats/strided/dmidrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmidrange + +[@stdlib/stats/strided/dmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmin + +[@stdlib/stats/strided/dminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dminabs + +[@stdlib/stats/strided/dminsorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dminsorted + +[@stdlib/stats/strided/dmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmskmax + +[@stdlib/stats/strided/dmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmskmin + +[@stdlib/stats/strided/dmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dmskrange + +[@stdlib/stats/strided/dnanmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmax + +[@stdlib/stats/strided/dnanmaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmaxabs + +[@stdlib/stats/strided/dnanmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmean + +[@stdlib/stats/strided/dnanmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmeanors + +[@stdlib/stats/strided/dnanmeanpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmeanpn + +[@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/dnanmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmin + +[@stdlib/stats/strided/dnanminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanminabs + +[@stdlib/stats/strided/dnanrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanrange + +[@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/strided/dsemch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dsemch + +[@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/strided/dsmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/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/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/strided/dvarmtk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dvarmtk + +[@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/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/strided/smeanli]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/smeanli + +[@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/strided/smidrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/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/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/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 + +[@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/strided/sstdevyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/sstdevyc + +[@stdlib/stats/strided/svariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/svariancech + +[@stdlib/stats/strided/svariancepn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/svariancepn + +[@stdlib/stats/strided/svariancetk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/svariancetk + +[@stdlib/stats/strided/svarianceyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/svarianceyc + + +