README.md

Base Statistics

Base (i.e., lower-level) statistical functions.

Usage

var stats = require( '@stdlib/stats/base' );

stats

Namespace containing "base" (i.e., lower-level) statistical functions.

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

The namespace contains the following sub-namespaces:

• dists: base (i.e., lower-level) probability distribution modules.

The namespace contains the following statistical functions:

• cumax( N, x, strideX, y, strideY ): calculate the cumulative maximum of a strided array.
• cumaxabs( N, x, strideX, y, strideY ): calculate the cumulative maximum absolute value of a strided array.
• cumin( N, x, strideX, y, strideY ): calculate the cumulative minimum of a strided array.
• cuminabs( N, x, strideX, y, strideY ): calculate the cumulative minimum absolute value of a strided array.
• dcumin( N, x, strideX, y, strideY ): calculate the cumulative minimum of double-precision floating-point strided array elements.
• dmean( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array.
• dmeanpn( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using a two-pass error correction algorithm.
• dmeanstdev( N, correction, x, strideX, out, strideOut ): calculate the mean and standard deviation of a double-precision floating-point strided array.
• dmeanstdevpn( N, correction, x, strideX, out, strideOut ): 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 ): calculate the mean and variance of a double-precision floating-point strided array.
• dmeanvarpn( N, correction, x, strideX, out, strideOut ): calculate the mean and variance of a double-precision floating-point strided array using a two-pass algorithm.
• dmskmax( N, x, strideX, mask, strideMask ): calculate the maximum value of a double-precision floating-point strided array according to a mask.
• dmskmin( N, x, strideX, mask, strideMask ): calculate the minimum value of a double-precision floating-point strided array according to a mask.
• dmskrange( N, x, strideX, mask, strideMask ): calculate the range of a double-precision floating-point strided array according to a mask.
• dnanmeanpn( N, x, strideX ): 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 ): calculate the arithmetic mean of a double-precision floating-point strided array, ignoring NaN values and using pairwise summation.
• dnanmeanwd( N, x, strideX ): calculate the arithmetic mean of a double-precision floating-point strided array, using Welford's algorithm and ignoring NaN values.
• dnanmin( N, x, strideX ): calculate the minimum value of a double-precision floating-point strided array, ignoring NaN values.
• dnanminabs( N, x, strideX ): calculate the minimum absolute value of a double-precision floating-point strided array, ignoring NaN values.
• dnanmskmax( N, x, strideX, mask, strideMask ): 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 ): 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 ): calculate the range of a double-precision floating-point strided array according to a mask, ignoring NaN values.
• dnanrange( N, x, strideX ): calculate the range of a double-precision floating-point strided array, ignoring NaN values.
• dnanstdev( N, correction, x, stride ): calculate the standard deviation of a double-precision floating-point strided array ignoring NaN values.
• dnanstdevch( N, correction, x, strideX ): 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 ): 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 ): 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 ): 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 ): 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 ): calculate the variance of a double-precision floating-point strided array ignoring NaN values.
• dnanvariancech( N, correction, x, strideX ): 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 ): 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 ): 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 ): calculate the variance of a double-precision floating-point strided array ignoring NaN values and using Welford's algorithm.
• dnanvarianceyc( N, correction, x, strideX ): 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 ): calculate the range of a double-precision floating-point strided array.
• dsem( N, correction, x, stride ): calculate the standard error of the mean of a double-precision floating-point strided array.
• dsemch( N, correction, x, strideX ): 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 ): 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 ): 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 ): calculate the standard error of the mean of a double-precision floating-point strided array using Welford's algorithm.
• dsemyc( N, correction, x, strideX ): 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 ): 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 ): 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 ): 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 ): 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 ): 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 ): 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 ): 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 ): 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 ): 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 ): calculate the standard deviation of a double-precision floating-point strided array.
• dstdevch( N, correction, x, strideX ): calculate the standard deviation of a double-precision floating-point strided array using a one-pass trial mean algorithm.
• dstdevpn( N, correction, x, strideX ): calculate the standard deviation of a double-precision floating-point strided array using a two-pass algorithm.
• dstdevtk( N, correction, x, strideX ): calculate the standard deviation of a double-precision floating-point strided array using a one-pass textbook algorithm.
• dstdevwd( N, correction, x, strideX ): calculate the standard deviation of a double-precision floating-point strided array using Welford's algorithm.
• dstdevyc( N, correction, x, strideX ): calculate the standard deviation of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.
• dsvariance( N, correction, x, stride ): 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 ): 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 ): calculate the variance of a double-precision floating-point strided array.
• dvariancech( N, correction, x, strideX ): calculate the variance of a double-precision floating-point strided array using a one-pass trial mean algorithm.
• dvariancepn( N, correction, x, strideX ): calculate the variance of a double-precision floating-point strided array using a two-pass algorithm.
• dvariancetk( N, correction, x, strideX ): calculate the variance of a double-precision floating-point strided array using a one-pass textbook algorithm.
• dvariancewd( N, correction, x, strideX ): calculate the variance of a double-precision floating-point strided array using Welford's algorithm.
• dvarianceyc( N, correction, x, strideX ): 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 ): calculate the variance of a double-precision floating-point strided array provided a known mean.
• dvarmpn( N, mean, correction, x, stride ): 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 ): 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] ): calculate the maximum value of a strided array via a callback function.
• max( N, x, strideX ): calculate the maximum value of a strided array.
• maxabs( N, x, strideX ): calculate the maximum absolute value of a strided array.
• maxsorted( N, x, strideX ): calculate the maximum value of a sorted strided array.
• mean( N, x, stride ): calculate the arithmetic mean of a strided array.
• meankbn( N, x, stride ): calculate the arithmetic mean of a strided array using an improved Kahan–Babuška algorithm.
• meankbn2( N, x, stride ): calculate the arithmetic mean of a strided array using a second-order iterative Kahan–Babuška algorithm.
• meanors( N, x, stride ): calculate the arithmetic mean of a strided array using ordinary recursive summation.
• meanpn( N, x, stride ): calculate the arithmetic mean of a strided array using a two-pass error correction algorithm.
• meanpw( N, x, stride ): calculate the arithmetic mean of a strided array using pairwise summation.
• meanwd( N, x, stride ): calculate the arithmetic mean of a strided array using Welford's algorithm.
• mediansorted( N, x, strideX ): calculate the median value of a sorted strided array.
• minBy( N, x, stride, clbk[, thisArg] ): calculate the minimum value of a strided array via a callback function.
• min( N, x, strideX ): calculate the minimum value of a strided array.
• minabs( N, x, strideX ): calculate the minimum absolute value of a strided array.
• minsorted( N, x, strideX ): calculate the minimum value of a sorted strided array.
• mskmax( N, x, strideX, mask, strideMask ): calculate the maximum value of a strided array according to a mask.
• mskmin( N, x, strideX, mask, strideMask ): calculate the minimum value of a strided array according to a mask.
• mskrange( N, x, strideX, mask, strideMask ): calculate the range of a strided array according to a mask.
• nanmaxBy( N, x, stride, clbk[, thisArg] ): calculate the maximum value of a strided array via a callback function, ignoring NaN values.
• nanmax( N, x, strideX ): calculate the maximum value of a strided array, ignoring NaN values.
• nanmaxabs( N, x, stride ): calculate the maximum absolute value of a strided array, ignoring NaN values.
• nanmean( N, x, stride ): calculate the arithmetic mean of a strided array, ignoring NaN values.
• nanmeanors( N, x, stride ): calculate the arithmetic mean of a strided array, ignoring NaN values and using ordinary recursive summation.
• nanmeanpn( N, x, stride ): calculate the arithmetic mean of a strided array, ignoring NaN values and using a two-pass error correction algorithm.
• nanmeanwd( N, x, stride ): calculate the arithmetic mean of a strided array, ignoring NaN values and using Welford's algorithm.
• nanminBy( N, x, stride, clbk[, thisArg] ): calculate the minimum value of a strided array via a callback function, ignoring NaN values.
• nanmin( N, x, stride ): calculate the minimum value of a strided array, ignoring NaN values.
• nanminabs( N, x, stride ): calculate the minimum absolute value of a strided array, ignoring NaN values.
• nanmskmax( N, x, strideX, mask, strideMask ): calculate the maximum value of a strided array according to a mask, ignoring NaN values.
• nanmskmin( N, x, strideX, mask, strideMask ): calculate the minimum value of a strided array according to a mask, ignoring NaN values.
• nanmskrange( N, x, strideX, mask, strideMask ): calculate the range of a strided array according to a mask, ignoring NaN values.
• nanrangeBy( N, x, stride, clbk[, thisArg] ): calculate the range of a strided array via a callback function, ignoring NaN values.
• nanrange( N, x, stride ): calculate the range of a strided array, ignoring NaN values.
• nanstdev( N, correction, x, stride ): calculate the standard deviation of a strided array ignoring NaN values.
• nanstdevch( N, correction, x, stride ): calculate the standard deviation of a strided array ignoring NaN values and using a one-pass trial mean algorithm.
• nanstdevpn( N, correction, x, stride ): calculate the standard deviation of a strided array ignoring NaN values and using a two-pass algorithm.
• nanstdevtk( N, correction, x, stride ): calculate the standard deviation of a strided array ignoring NaN values and using a one-pass textbook algorithm.
• nanstdevwd( N, correction, x, stride ): calculate the standard deviation of a strided array ignoring NaN values and using Welford's algorithm.
• nanstdevyc( N, correction, x, stride ): 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, stride ): calculate the variance of a strided array ignoring NaN values.
• nanvariancech( N, correction, x, stride ): calculate the variance of a strided array ignoring NaN values and using a one-pass trial mean algorithm.
• nanvariancepn( N, correction, x, stride ): calculate the variance of a strided array ignoring NaN values and using a two-pass algorithm.
• nanvariancetk( N, correction, x, stride ): calculate the variance of a strided array ignoring NaN values and using a one-pass textbook algorithm.
• nanvariancewd( N, correction, x, stride ): calculate the variance of a strided array ignoring NaN values and using Welford's algorithm.
• nanvarianceyc( N, correction, x, stride ): 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] ): calculate the range of a strided array via a callback function.
• range( N, x, stride ): calculate the range of a strided array.
• scumax( N, x, strideX, y, strideY ): calculate the cumulative maximum of single-precision floating-point strided array elements.
• scumaxabs( N, x, strideX, y, strideY ): calculate the cumulative maximum absolute value of single-precision floating-point strided array elements.
• scumin( N, x, strideX, y, strideY ): calculate the cumulative minimum of single-precision floating-point strided array elements.
• scuminabs( N, x, strideX, y, strideY ): calculate the cumulative minimum absolute value of single-precision floating-point strided array elements.
• sdsmean( N, x, strideX ): calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation.
• sdsmeanors( N, x, strideX ): calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation with extended accumulation.
• sdsnanmean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using extended accumulation.
• sdsnanmeanors( N, x, stride ): 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 ): calculate the maximum value of a single-precision floating-point strided array.
• smaxabs( N, x, strideX ): calculate the maximum absolute value of a single-precision floating-point strided array.
• smaxabssorted( N, x, strideX ): calculate the maximum absolute value of a sorted single-precision floating-point strided array.
• smaxsorted( N, x, stride ): calculate the maximum value of a sorted single-precision floating-point strided array.
• smean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array.
• smeankbn( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using an improved Kahan–Babuška algorithm.
• smeankbn2( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm.
• smeanli( N, x, strideX ): calculate the arithmetic mean of a single-precision floating-point strided array using a one-pass trial mean algorithm.
• smeanlipw( N, x, stride ): 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 ): calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation.
• smeanpn( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm.
• smeanpw( N, x, strideX ): calculate the arithmetic mean of a single-precision floating-point strided array using pairwise summation.
• smeanwd( N, x, strideX ): calculate the arithmetic mean of a single-precision floating-point strided array using Welford's algorithm.
• smediansorted( N, x, strideX ): calculate the median value of a sorted single-precision floating-point strided array.
• smidrange( N, x, strideX ): calculate the mid-range of a single-precision floating-point strided array.
• smin( N, x, strideX ): calculate the minimum value of a single-precision floating-point strided array.
• sminabs( N, x, strideX ): calculate the minimum absolute value of a single-precision floating-point strided array.
• sminsorted( N, x, strideX ): calculate the minimum value of a sorted single-precision floating-point strided array.
• smskmax( N, x, strideX, mask, strideMask ): calculate the maximum value of a single-precision floating-point strided array according to a mask.
• smskmin( N, x, strideX, mask, strideMask ): calculate the minimum value of a single-precision floating-point strided array according to a mask.
• smskrange( N, x, strideX, mask, strideMask ): calculate the range of a single-precision floating-point strided array according to a mask.
• snanmax( N, x, strideX ): calculate the maximum value of a single-precision floating-point strided array, ignoring NaN values.
• snanmaxabs( N, x, strideX ): calculate the maximum absolute value of a single-precision floating-point strided array, ignoring NaN values.
• snanmean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values.
• snanmeanors( N, x, strideX ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using ordinary recursive summation.
• snanmeanpn( N, x, strideX ): 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 ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using Welford's algorithm.
• snanmin( N, x, strideX ): calculate the minimum value of a single-precision floating-point strided array, ignoring NaN values.
• snanminabs( N, x, strideX ): calculate the minimum absolute value of a single-precision floating-point strided array, ignoring NaN values.
• snanmskmax( N, x, strideX, mask, strideMask ): 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 ): 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 ): calculate the range of a single-precision floating-point strided array according to a mask, ignoring NaN values.
• snanrange( N, x, strideX ): calculate the range of a single-precision floating-point strided array, ignoring NaN values.
• snanstdev( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values.
• snanstdevch( N, correction, x, stride ): 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 ): calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values and using a two-pass algorithm.
• snanstdevtk( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values and using a one-pass textbook algorithm.
• snanstdevwd( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values and using Welford's algorithm.
• snanstdevyc( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values and using a one-pass algorithm proposed by Youngs and Cramer.
• snanvariance( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoring NaN values.
• snanvariancech( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.
• snanvariancepn( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a two-pass algorithm.
• snanvariancetk( N, correction, x, stride ): 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 ): calculate the variance of a single-precision floating-point strided array ignoring NaN values and using Welford's algorithm.
• snanvarianceyc( N, correction, x, stride ): 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 ): calculate the range of a single-precision floating-point strided array.
• sstdev( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array.
• sstdevch( N, correction, x, strideX ): calculate the standard deviation of a single-precision floating-point strided array using a one-pass trial mean algorithm.
• sstdevpn( N, correction, x, strideX ): calculate the standard deviation of a single-precision floating-point strided array using a two-pass algorithm.
• sstdevtk( N, correction, x, strideX ): calculate the standard deviation of a single-precision floating-point strided array using a one-pass textbook algorithm.
• sstdevwd( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array using Welford's algorithm.
• sstdevyc( N, correction, x, strideX ): 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 ): calculate the standard deviation of a strided array.
• stdevch( N, correction, x, stride ): calculate the standard deviation of a strided array using a one-pass trial mean algorithm.
• stdevpn( N, correction, x, stride ): calculate the standard deviation of a strided array using a two-pass algorithm.
• stdevtk( N, correction, x, stride ): calculate the standard deviation of a strided array using a one-pass textbook algorithm.
• stdevwd( N, correction, x, stride ): calculate the standard deviation of a strided array using Welford's algorithm.
• stdevyc( N, correction, x, stride ): calculate the standard deviation of a strided array using a one-pass algorithm proposed by Youngs and Cramer.
• svariance( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array.
• svariancech( N, correction, x, strideX ): calculate the variance of a single-precision floating-point strided array using a one-pass trial mean algorithm.
• svariancepn( N, correction, x, strideX ): calculate the variance of a single-precision floating-point strided array using a two-pass algorithm.
• svariancetk( N, correction, x, strideX ): calculate the variance of a single-precision floating-point strided array using a one-pass textbook algorithm.
• svariancewd( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array using Welford's algorithm.
• svarianceyc( N, correction, x, strideX ): calculate the variance of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.
• variance( N, correction, x, stride ): calculate the variance of a strided array.
• variancech( N, correction, x, stride ): calculate the variance of a strided array using a one-pass trial mean algorithm.
• variancepn( N, correction, x, stride ): calculate the variance of a strided array using a two-pass algorithm.
• variancetk( N, correction, x, stride ): calculate the variance of a strided array using a one-pass textbook algorithm.
• variancewd( N, correction, x, stride ): calculate the variance of a strided array using Welford's algorithm.
• varianceyc( N, correction, x, stride ): calculate the variance of a strided array using a one-pass algorithm proposed by Youngs and Cramer.

Examples

var objectKeys = require( '@stdlib/utils/keys' );
var ns = require( '@stdlib/stats/base' );

console.log( objectKeys( ns ) );