@stdlib/stats-base NPM

Base Statistics

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

Installation

npm install @stdlib/stats-base

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.
dcumax( N, x, strideX, y, strideY ): calculate the cumulative maximum of double-precision floating-point strided array elements.
dcumaxabs( N, x, strideX, y, strideY ): calculate the cumulative maximum absolute value of double-precision floating-point strided array elements.
dcumin( N, x, strideX, y, strideY ): calculate the cumulative minimum of double-precision floating-point strided array elements.
dcuminabs( N, x, strideX, y, strideY ): calculate the cumulative minimum absolute value of double-precision floating-point strided array elements.
dmax( N, x, stride ): calculate the maximum value of a double-precision floating-point strided array.
dmaxabs( N, x, stride ): calculate the maximum absolute value of a double-precision floating-point strided array.
dmaxabssorted( N, x, stride ): calculate the maximum absolute value of a sorted double-precision floating-point strided array.
dmaxsorted( N, x, stride ): calculate the maximum value of a sorted double-precision floating-point strided array.
dmean( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array.
dmeankbn( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using an improved Kahan–Babuška algorithm.
dmeankbn2( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm.
dmeanli( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using a one-pass trial mean algorithm.
dmeanlipw( N, x, stride ): 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, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using ordinary recursive summation.
dmeanpn( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using a two-pass error correction algorithm.
dmeanpw( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using pairwise summation.
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.
dmeanwd( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array using Welford's algorithm.
dmediansorted( N, x, stride ): calculate the median value of a sorted double-precision floating-point strided array.
dmidrange( N, x, stride ): calculate the mid-range of a double-precision floating-point strided array.
dmin( N, x, stride ): calculate the minimum value of a double-precision floating-point strided array.
dminabs( N, x, stride ): calculate the minimum absolute value of a double-precision floating-point strided array.
dminsorted( N, x, stride ): calculate the minimum value of a sorted double-precision floating-point strided array.
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.
dnanmax( N, x, stride ): calculate the maximum value of a double-precision floating-point strided array, ignoring NaN values.
dnanmaxabs( N, x, stride ): calculate the maximum absolute value of a double-precision floating-point strided array, ignoring NaN values.
dnanmean( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array, ignoring NaN values.
dnanmeanors( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array, ignoring NaN values and using ordinary recursive summation.
dnanmeanpn( N, x, stride ): 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, stride ): calculate the arithmetic mean of a double-precision floating-point strided array, ignoring NaN values and using pairwise summation.
dnanmeanwd( N, x, stride ): calculate the arithmetic mean of a double-precision floating-point strided array, using Welford's algorithm and ignoring NaN values.
dnanmin( N, x, stride ): calculate the minimum value of a double-precision floating-point strided array, ignoring NaN values.
dnanminabs( N, x, stride ): 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, stride ): 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, stride ): calculate the standard deviation of a double-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.
dnanstdevpn( N, correction, x, stride ): 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, stride ): 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, stride ): calculate the standard deviation of a double-precision floating-point strided array ignoring NaN values and using Welford's algorithm.
dnanstdevyc( N, correction, x, stride ): calculate the standard deviation of a double-precision floating-point strided array ignoring NaN values and using a one-pass algorithm proposed by Youngs and Cramer.
dnanvariance( N, correction, x, stride ): calculate the variance of a double-precision floating-point strided array ignoring NaN values.
dnanvariancech( N, correction, x, stride ): 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, stride ): calculate the variance of a double-precision floating-point strided array ignoring NaN values and using a two-pass algorithm.
dnanvariancetk( N, correction, x, stride ): 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, stride ): calculate the variance of a double-precision floating-point strided array ignoring NaN values and using Welford's algorithm.
dnanvarianceyc( N, correction, x, stride ): 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, stride ): 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, stride ): 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, stride ): 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, stride ): calculate the standard error of the mean of a double-precision floating-point strided array using Welford's algorithm.
dsemyc( N, correction, x, stride ): 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, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation and returning an extended precision result.
dsmeanors( N, x, stride ): 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, stride ): 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, stride ): 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, stride ): 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, stride ): 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, stride ): 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, stride ): 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, stride ): 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, stride ): calculate the standard deviation of a double-precision floating-point strided array using a one-pass trial mean algorithm.
dstdevpn( N, correction, x, stride ): calculate the standard deviation of a double-precision floating-point strided array using a two-pass algorithm.
dstdevtk( N, correction, x, stride ): calculate the standard deviation of a double-precision floating-point strided array using a one-pass textbook algorithm.
dstdevwd( N, correction, x, stride ): calculate the standard deviation of a double-precision floating-point strided array using Welford's algorithm.
dstdevyc( N, correction, x, stride ): 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, stride ): 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, stride ): calculate the variance of a double-precision floating-point strided array.
dvariancech( N, correction, x, stride ): calculate the variance of a double-precision floating-point strided array using a one-pass trial mean algorithm.
dvariancepn( N, correction, x, stride ): calculate the variance of a double-precision floating-point strided array using a two-pass algorithm.
dvariancetk( N, correction, x, stride ): calculate the variance of a double-precision floating-point strided array using a one-pass textbook algorithm.
dvariancewd( N, correction, x, stride ): calculate the variance of a double-precision floating-point strided array using Welford's algorithm.
dvarianceyc( N, correction, x, stride ): 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, stride ): 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, stride ): calculate the maximum value of a strided array.
maxabs( N, x, stride ): calculate the maximum absolute value of a strided array.
maxsorted( N, x, stride ): 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, stride ): 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, stride ): calculate the minimum value of a strided array.
minabs( N, x, stride ): calculate the minimum absolute value of a strided array.
minsorted( N, x, stride ): 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, stride ): 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, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation.
sdsmeanors( N, x, stride ): 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, stride ): calculate the maximum value of a single-precision floating-point strided array.
smaxabs( N, x, stride ): calculate the maximum absolute value of a single-precision floating-point strided array.
smaxabssorted( N, x, stride ): 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, stride ): 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, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using pairwise summation.
smeanwd( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array using Welford's algorithm.
smediansorted( N, x, stride ): calculate the median value of a sorted single-precision floating-point strided array.
smidrange( N, x, stride ): calculate the mid-range of a single-precision floating-point strided array.
smin( N, x, stride ): calculate the minimum value of a single-precision floating-point strided array.
sminabs( N, x, stride ): calculate the minimum absolute value of a single-precision floating-point strided array.
sminsorted( N, x, stride ): 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, stride ): calculate the maximum value of a single-precision floating-point strided array, ignoring NaN values.
snanmaxabs( N, x, stride ): 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, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using ordinary recursive summation.
snanmeanpn( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using a two-pass error correction algorithm.
snanmeanwd( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using Welford's algorithm.
snanmin( N, x, stride ): calculate the minimum value of a single-precision floating-point strided array, ignoring NaN values.
snanminabs( N, x, stride ): 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, stride ): 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, stride ): 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, stride ): calculate the standard deviation of a single-precision floating-point strided array using a one-pass trial mean algorithm.
sstdevpn( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array using a two-pass algorithm.
sstdevtk( N, correction, x, stride ): 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, stride ): 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, stride ): calculate the variance of a single-precision floating-point strided array using a one-pass trial mean algorithm.
svariancepn( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array using a two-pass algorithm.
svariancetk( N, correction, x, stride ): 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, stride ): 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 ) );

Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.