Statistics

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Statistical operations for strided arrays.

Installation

npm install @stdlib/stats-strided

Usage

var ns = require( '@stdlib/stats-strided' );

ns

Namespace containing APIs for performing statistical operations on strided arrays.

var o = ns;
// returns {...}

The namespace exports the following:

• [covarmtk( N, correction, meanx, x, strideX, meany, y, strideY )][@stdlib/stats/strided/covarmtk]: calculate the covariance of two strided arrays provided known means and using a one-pass textbook algorithm.
• [dcovarmtk( N, correction, meanx, x, strideX, meany, y, strideY )][@stdlib/stats/strided/dcovarmtk]: calculate the covariance of two double-precision floating-point strided arrays provided known means and using a one-pass textbook algorithm.
• [dcovmatmtk( order, orient, uplo, M, N, correction, means, strideM, A, LDA, B, LDB )][@stdlib/stats/strided/dcovmatmtk]: compute the covariance matrix for an M by N double-precision floating-point matrix A and assign the results to a matrix B when provided known means and using a one-pass textbook algorithm.
• [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.
• [distances][@stdlib/stats/strided/distances]: distance metrics for strided arrays.
• [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.
• [dmean( N, x, strideX )][@stdlib/stats/strided/dmean]: calculate the arithmetic mean of a double-precision floating-point strided array.
• [dmeankbn( N, x, strideX )][@stdlib/stats/strided/dmeankbn]: calculate the arithmetic mean of a double-precision floating-point strided array using an improved Kahan–Babuška algorithm.
• [dmeankbn2( N, x, strideX )][@stdlib/stats/strided/dmeankbn2]: calculate the arithmetic mean of a double-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm.
• [dmeanli( N, x, strideX )][@stdlib/stats/strided/dmeanli]: calculate the arithmetic mean of a double-precision floating-point strided array using a one-pass trial mean algorithm.
• [dmeanlipw( N, x, strideX )][@stdlib/stats/strided/dmeanlipw]: calculate the arithmetic mean of a double-precision floating-point strided array using a one-pass trial mean algorithm with pairwise summation.
• [dmeanors( N, x, strideX )][@stdlib/stats/strided/dmeanors]: calculate the arithmetic mean of a double-precision floating-point strided array using ordinary recursive summation.
• [dmeanpn( N, x, strideX )][@stdlib/stats/strided/dmeanpn]: calculate the arithmetic mean of a double-precision floating-point strided array using a two-pass error correction algorithm.
• [dmeanpw( N, x, strideX )][@stdlib/stats/strided/dmeanpw]: calculate the arithmetic mean of a double-precision floating-point strided array using pairwise summation.
• [dmeanstdev( N, correction, x, strideX, out, strideOut )][@stdlib/stats/strided/dmeanstdev]: calculate the mean and standard deviation of a double-precision floating-point strided array.
• [dmeanstdevpn( N, correction, x, strideX, out, strideOut )][@stdlib/stats/strided/dmeanstdevpn]: calculate the mean and standard deviation of a double-precision floating-point strided array using a two-pass algorithm.
• [dmeanvar( N, correction, x, strideX, out, strideOut )][@stdlib/stats/strided/dmeanvar]: calculate the mean and variance of a double-precision floating-point strided array.
• [dmeanvarpn( N, correction, x, strideX, out, strideOut )][@stdlib/stats/strided/dmeanvarpn]: calculate the mean and variance of a double-precision floating-point strided array using a two-pass algorithm.
• [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.
• [dmskmaxabs( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/dmskmaxabs]: calculate the maximum absolute value of a double-precision floating-point strided array according to a mask.
• [dmskmidrange( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/dmskmidrange]: calculate the mid-range 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.
• [dnanmidrange( N, x, strideX )][@stdlib/stats/strided/dnanmidrange]: calculate the mid-range of a double-precision floating-point strided array, ignoring NaN values.
• [dnanmin( N, x, strideX )][@stdlib/stats/strided/dnanmin]: calculate the minimum value of a double-precision floating-point strided array, ignoring NaN values.
• [dnanminabs( N, x, strideX )][@stdlib/stats/strided/dnanminabs]: calculate the minimum absolute value of a double-precision floating-point strided array, ignoring NaN values.
• [dnanmskmax( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/dnanmskmax]: calculate the maximum value of a double-precision floating-point strided array according to a mask, ignoring NaN values.
• [dnanmskmidrange( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/dnanmskmidrange]: calculate the mid-range of a double-precision floating-point strided array according to a mask, ignoring NaN values.
• [dnanmskmin( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/dnanmskmin]: calculate the minimum value of a double-precision floating-point strided array according to a mask, ignoring NaN values.
• [dnanmskrange( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/dnanmskrange]: calculate the range of a double-precision floating-point strided array according to a mask, ignoring NaN values.
• [dnanrange( N, x, strideX )][@stdlib/stats/strided/dnanrange]: calculate the range of a double-precision floating-point strided array, ignoring NaN values.
• [dnanstdev( N, correction, x, strideX )][@stdlib/stats/strided/dnanstdev]: calculate the standard deviation of a double-precision floating-point strided array ignoring NaN values.
• [dnanstdevch( N, correction, x, strideX )][@stdlib/stats/strided/dnanstdevch]: calculate the standard deviation of a double-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.
• [dnanstdevpn( N, correction, x, strideX )][@stdlib/stats/strided/dnanstdevpn]: calculate the standard deviation of a double-precision floating-point strided array, ignoring NaN values and using a two-pass algorithm.
• [dnanstdevtk( N, correction, x, strideX )][@stdlib/stats/strided/dnanstdevtk]: calculate the standard deviation of a double-precision floating-point strided array ignoring NaN values and using a one-pass textbook algorithm.
• [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.
• [drangeabs( N, x, strideX )][@stdlib/stats/strided/drangeabs]: compute the range of absolute values of a double-precision floating-point strided array.
• [dsem( N, correction, x, strideX )][@stdlib/stats/strided/dsem]: calculate the standard error of the mean of a double-precision floating-point strided array.
• [dsemch( N, correction, x, strideX )][@stdlib/stats/strided/dsemch]: calculate the standard error of the mean of a double-precision floating-point strided array using a one-pass trial mean algorithm.
• [dsempn( N, correction, x, strideX )][@stdlib/stats/strided/dsempn]: calculate the standard error of the mean of a double-precision floating-point strided array using a two-pass algorithm.
• [dsemtk( N, correction, x, strideX )][@stdlib/stats/strided/dsemtk]: calculate the standard error of the mean of a double-precision floating-point strided array using a one-pass textbook algorithm.
• [dsemwd( N, correction, x, strideX )][@stdlib/stats/strided/dsemwd]: calculate the standard error of the mean of a double-precision floating-point strided array using Welford's algorithm.
• [dsemyc( N, correction, x, strideX )][@stdlib/stats/strided/dsemyc]: calculate the standard error of the mean of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.
• [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.
• [dstdev( N, correction, x, strideX )][@stdlib/stats/strided/dstdev]: calculate the standard deviation of a double-precision floating-point strided array.
• [dstdevch( N, correction, x, strideX )][@stdlib/stats/strided/dstdevch]: calculate the standard deviation of a double-precision floating-point strided array using a one-pass trial mean algorithm.
• [dstdevpn( N, correction, x, strideX )][@stdlib/stats/strided/dstdevpn]: calculate the standard deviation of a double-precision floating-point strided array using a two-pass algorithm.
• [dstdevtk( N, correction, x, strideX )][@stdlib/stats/strided/dstdevtk]: calculate the standard deviation of a double-precision floating-point strided array using a one-pass textbook algorithm.
• [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, correction, mean, x, strideX )][@stdlib/stats/strided/dvarm]: calculate the variance of a double-precision floating-point strided array provided a known mean.
• [dvarmpn( N, correction, mean, x, strideX )][@stdlib/stats/strided/dvarmpn]: calculate the variance of a double-precision floating-point strided array provided a known mean and using Neely's correction algorithm.
• [dvarmtk( N, correction, mean, 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.
• [dztest( N, alternative, alpha, mu, sigma, x, strideX, out )][@stdlib/stats/strided/dztest]: compute a one-sample Z-test for a double-precision floating-point strided array.
• [dztest2( NX, NY, alternative, alpha, diff, sigmax, x, strideX, sigmay, y, strideY, out )][@stdlib/stats/strided/dztest2]: compute a two-sample Z-test for two double-precision floating-point strided arrays.
• [maxBy( N, x, strideX, clbk[, thisArg] )][@stdlib/stats/strided/max-by]: calculate the maximum value of a strided array via a callback function.
• [max( N, x, strideX )][@stdlib/stats/strided/max]: calculate the maximum value of a strided array.
• [maxabs( N, x, strideX )][@stdlib/stats/strided/maxabs]: calculate the maximum absolute value of a strided array.
• [maxsorted( N, x, strideX )][@stdlib/stats/strided/maxsorted]: calculate the maximum value of a sorted strided array.
• [mean( N, x, strideX )][@stdlib/stats/strided/mean]: calculate the arithmetic mean of a strided array.
• [meankbn( N, x, strideX )][@stdlib/stats/strided/meankbn]: calculate the arithmetic mean of a strided array using an improved Kahan–Babuška algorithm.
• [meankbn2( N, x, strideX )][@stdlib/stats/strided/meankbn2]: calculate the arithmetic mean of a strided array using a second-order iterative Kahan–Babuška algorithm.
• [meanors( N, x, strideX )][@stdlib/stats/strided/meanors]: calculate the arithmetic mean of a strided array using ordinary recursive summation.
• [meanpn( N, x, strideX )][@stdlib/stats/strided/meanpn]: calculate the arithmetic mean of a strided array using a two-pass error correction algorithm.
• [meanpw( N, x, strideX )][@stdlib/stats/strided/meanpw]: calculate the arithmetic mean of a strided array using pairwise summation.
• [meanwd( N, x, strideX )][@stdlib/stats/strided/meanwd]: calculate the arithmetic mean of a strided array using Welford's algorithm.
• [mediansorted( N, x, strideX )][@stdlib/stats/strided/mediansorted]: calculate the median value of a sorted strided array.
• [midrangeBy( N, x, strideX, clbk[, thisArg] )][@stdlib/stats/strided/midrange-by]: calculate the mid-range of a strided array via a callback function.
• [midrange( N, x, strideX )][@stdlib/stats/strided/midrange]: calculate the mid-range of a strided array.
• [minBy( N, x, strideX, clbk[, thisArg] )][@stdlib/stats/strided/min-by]: calculate the minimum value of a strided array via a callback function.
• [min( N, x, strideX )][@stdlib/stats/strided/min]: calculate the minimum value of a strided array.
• [minabs( N, x, strideX )][@stdlib/stats/strided/minabs]: calculate the minimum absolute value of a strided array.
• [minsorted( N, x, strideX )][@stdlib/stats/strided/minsorted]: calculate the minimum value of a sorted strided array.
• [mskmax( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/mskmax]: calculate the maximum value of a strided array according to a mask.
• [mskmaxabs( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/mskmaxabs]: calculate the maximum absolute value of a strided array according to a mask.
• [mskmidrange( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/mskmidrange]: calculate the mid-range of a strided array according to a mask.
• [mskmin( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/mskmin]: calculate the minimum value of a strided array according to a mask.
• [mskrange( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/mskrange]: calculate the range of a strided array according to a mask.
• [nanmaxBy( N, x, strideX, clbk[, thisArg] )][@stdlib/stats/strided/nanmax-by]: calculate the maximum value of a strided array via a callback function, ignoring NaN values.
• [nanmax( N, x, strideX )][@stdlib/stats/strided/nanmax]: calculate the maximum value of a strided array, ignoring NaN values.
• [nanmaxabs( N, x, strideX )][@stdlib/stats/strided/nanmaxabs]: calculate the maximum absolute value of a strided array, ignoring NaN values.
• [nanmean( N, x, strideX )][@stdlib/stats/strided/nanmean]: calculate the arithmetic mean of a strided array, ignoring NaN values.
• [nanmeanors( N, x, strideX )][@stdlib/stats/strided/nanmeanors]: calculate the arithmetic mean of a strided array, ignoring NaN values and using ordinary recursive summation.
• [nanmeanpn( N, x, strideX )][@stdlib/stats/strided/nanmeanpn]: calculate the arithmetic mean of a strided array, ignoring NaN values and using a two-pass error correction algorithm.
• [nanmeanwd( N, x, strideX )][@stdlib/stats/strided/nanmeanwd]: calculate the arithmetic mean of a strided array, ignoring NaN values and using Welford's algorithm.
• [nanmidrangeBy( N, x, strideX, clbk[, thisArg] )][@stdlib/stats/strided/nanmidrange-by]: calculate the mid-range of a strided array via a callback function, ignoring NaN values.
• [nanmidrange( N, x, strideX )][@stdlib/stats/strided/nanmidrange]: calculate the mid-range of a strided array, ignoring NaN values.
• [nanminBy( N, x, strideX, clbk[, thisArg] )][@stdlib/stats/strided/nanmin-by]: calculate the minimum value of a strided array via a callback function, ignoring NaN values.
• [nanmin( N, x, strideX )][@stdlib/stats/strided/nanmin]: calculate the minimum value of a strided array, ignoring NaN values.
• [nanminabs( N, x, strideX )][@stdlib/stats/strided/nanminabs]: calculate the minimum absolute value of a strided array, ignoring NaN values.
• [nanmskmax( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/nanmskmax]: calculate the maximum value of a strided array according to a mask, ignoring NaN values.
• [nanmskmidrange( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/nanmskmidrange]: calculate the mid-range of a strided array according to a mask, ignoring NaN values.
• [nanmskmin( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/nanmskmin]: calculate the minimum value of a strided array according to a mask, ignoring NaN values.
• [nanmskrange( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/nanmskrange]: calculate the range of a strided array according to a mask, ignoring NaN values.
• [nanrangeBy( N, x, strideX, clbk[, thisArg] )][@stdlib/stats/strided/nanrange-by]: calculate the range of a strided array via a callback function, ignoring NaN values.
• [nanrange( N, x, strideX )][@stdlib/stats/strided/nanrange]: calculate the range of a strided array, ignoring NaN values.
• [nanstdev( N, correction, x, strideX )][@stdlib/stats/strided/nanstdev]: calculate the standard deviation of a strided array ignoring NaN values.
• [nanstdevch( N, correction, x, strideX )][@stdlib/stats/strided/nanstdevch]: calculate the standard deviation of a strided array ignoring NaN values and using a one-pass trial mean algorithm.
• [nanstdevpn( N, correction, x, strideX )][@stdlib/stats/strided/nanstdevpn]: calculate the standard deviation of a strided array ignoring NaN values and using a two-pass algorithm.
• [nanstdevtk( N, correction, x, strideX )][@stdlib/stats/strided/nanstdevtk]: calculate the standard deviation of a strided array ignoring NaN values and using a one-pass textbook algorithm.
• [nanstdevwd( N, correction, x, strideX )][@stdlib/stats/strided/nanstdevwd]: calculate the standard deviation of a strided array ignoring NaN values and using Welford's algorithm.
• [nanstdevyc( N, correction, x, strideX )][@stdlib/stats/strided/nanstdevyc]: calculate the standard deviation of a strided array ignoring NaN values and using a one-pass algorithm proposed by Youngs and Cramer.
• [nanvariance( N, correction, x, strideX )][@stdlib/stats/strided/nanvariance]: calculate the variance of a strided array ignoring NaN values.
• [nanvariancech( N, correction, x, strideX )][@stdlib/stats/strided/nanvariancech]: calculate the variance of a strided array ignoring NaN values and using a one-pass trial mean algorithm.
• [nanvariancepn( N, correction, x, strideX )][@stdlib/stats/strided/nanvariancepn]: calculate the variance of a strided array ignoring NaN values and using a two-pass algorithm.
• [nanvariancetk( N, correction, x, strideX )][@stdlib/stats/strided/nanvariancetk]: calculate the variance of a strided array ignoring NaN values and using a one-pass textbook algorithm.
• [nanvariancewd( N, correction, x, strideX )][@stdlib/stats/strided/nanvariancewd]: calculate the variance of a strided array ignoring NaN values and using Welford's algorithm.
• [nanvarianceyc( N, correction, x, strideX )][@stdlib/stats/strided/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, strideX, clbk[, thisArg] )][@stdlib/stats/strided/range-by]: calculate the range of a strided array via a callback function.
• [range( N, x, strideX )][@stdlib/stats/strided/range]: calculate the range of a strided array.
• [rangeabs( N, x, strideX )][@stdlib/stats/strided/rangeabs]: calculate the range of absolute values of a strided array.
• [scovarmtk( N, correction, meanx, x, strideX, meany, y, strideY )][@stdlib/stats/strided/scovarmtk]: calculate the covariance of two single-precision floating-point strided arrays provided known means 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.
• [sdsnanmeanors( N, x, strideX )][@stdlib/stats/strided/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, strideX )][@stdlib/stats/strided/smean]: calculate the arithmetic mean of a single-precision floating-point strided array.
• [smeankbn( N, x, strideX )][@stdlib/stats/strided/smeankbn]: calculate the arithmetic mean of a single-precision floating-point strided array using an improved Kahan–Babuška algorithm.
• [smeankbn2( N, x, strideX )][@stdlib/stats/strided/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, strideX )][@stdlib/stats/strided/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, strideX )][@stdlib/stats/strided/smeanors]: calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation.
• [smeanpn( N, x, strideX )][@stdlib/stats/strided/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/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.
• [smskmaxabs( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/smskmaxabs]: calculate the maximum absolute value of a single-precision floating-point strided array according to a mask.
• [smskmidrange( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/smskmidrange]: calculate the mid-range 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, strideX )][@stdlib/stats/strided/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.
• [snanmidrange( N, x, strideX )][@stdlib/stats/strided/snanmidrange]: calculate the mid-range of a single-precision floating-point strided array, ignoring NaN values.
• [snanmin( N, x, strideX )][@stdlib/stats/strided/snanmin]: calculate the minimum value of a single-precision floating-point strided array, ignoring NaN values.
• [snanminabs( N, x, strideX )][@stdlib/stats/strided/snanminabs]: calculate the minimum absolute value of a single-precision floating-point strided array, ignoring NaN values.
• [snanmskmax( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/snanmskmax]: calculate the maximum value of a single-precision floating-point strided array according to a mask, ignoring NaN values.
• [snanmskmidrange( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/snanmskmidrange]: calculate the mid-range of a single-precision floating-point strided array according to a mask, ignoring NaN values.
• [snanmskmin( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/snanmskmin]: calculate the minimum value of a single-precision floating-point strided array according to a mask, ignoring NaN values.
• [snanmskrange( N, x, strideX, mask, strideMask )][@stdlib/stats/strided/snanmskrange]: calculate the range of a single-precision floating-point strided array according to a mask, ignoring NaN values.
• [snanrange( N, x, strideX )][@stdlib/stats/strided/snanrange]: calculate the range of a single-precision floating-point strided array, ignoring NaN values.
• [srange( N, x, strideX )][@stdlib/stats/strided/srange]: calculate the range of a single-precision floating-point strided array.
• [srangeabs( N, x, strideX )][@stdlib/stats/strided/srangeabs]: compute the range of absolute values of a single-precision floating-point strided array.
• [sstdev( N, correction, x, strideX )][@stdlib/stats/strided/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, strideX )][@stdlib/stats/strided/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, strideX )][@stdlib/stats/strided/stdev]: calculate the standard deviation of a strided array.
• [stdevch( N, correction, x, strideX )][@stdlib/stats/strided/stdevch]: calculate the standard deviation of a strided array using a one-pass trial mean algorithm.
• [stdevpn( N, correction, x, strideX )][@stdlib/stats/strided/stdevpn]: calculate the standard deviation of a strided array using a two-pass algorithm.
• [stdevtk( N, correction, x, strideX )][@stdlib/stats/strided/stdevtk]: calculate the standard deviation of a strided array using a one-pass textbook algorithm.
• [stdevwd( N, correction, x, strideX )][@stdlib/stats/strided/stdevwd]: calculate the standard deviation of a strided array using Welford's algorithm.
• [stdevyc( N, correction, x, strideX )][@stdlib/stats/strided/stdevyc]: calculate the standard deviation of a strided array using a one-pass algorithm proposed by Youngs and Cramer.
• [svariance( N, correction, x, strideX )][@stdlib/stats/strided/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/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.
• [svariancewd( N, correction, x, strideX )][@stdlib/stats/strided/svariancewd]: calculate the variance of a single-precision floating-point strided array using Welford's 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.
• [sztest( N, alternative, alpha, mu, sigma, x, strideX, out )][@stdlib/stats/strided/sztest]: compute a one-sample Z-test for a single-precision floating-point strided array.
• [sztest2( NX, NY, alternative, alpha, diff, sigmax, x, strideX, sigmay, y, strideY, out )][@stdlib/stats/strided/sztest2]: compute a two-sample Z-test for two single-precision floating-point strided arrays.
• [variance( N, correction, x, strideX )][@stdlib/stats/strided/variance]: calculate the variance of a strided array.
• [variancech( N, correction, x, strideX )][@stdlib/stats/strided/variancech]: calculate the variance of a strided array using a one-pass trial mean algorithm.
• [variancepn( N, correction, x, strideX )][@stdlib/stats/strided/variancepn]: calculate the variance of a strided array using a two-pass algorithm.
• [variancetk( N, correction, x, strideX )][@stdlib/stats/strided/variancetk]: calculate the variance of a strided array using a one-pass textbook algorithm.
• [variancewd( N, correction, x, strideX )][@stdlib/stats/strided/variancewd]: calculate the variance of a strided array using Welford's algorithm.
• [varianceyc( N, correction, x, strideX )][@stdlib/stats/strided/varianceyc]<span class="delim