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.
• dmean( N, x, strideX ): calculate the arithmetic mean of a double-precision floating-point strided array.
• dmeanpn( N, x, strideX ): 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.
• 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.
• dnanstdev( N, correction, x, stride ): calculate the standard deviation of a double-precision floating-point strided array ignoring NaN values.
• dsem( N, correction, x, strideX ): calculate the standard error of the mean of a double-precision floating-point strided array.
• dsempn( N, correction, x, strideX ): calculate the standard error of the mean of a double-precision floating-point strided array using a two-pass algorithm.
• dstdev( N, correction, x, strideX ): calculate the standard deviation of a double-precision floating-point strided array.
• 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.
• 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, strideX ): 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, strideX ): 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, strideX ): 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, strideX ): calculate the variance of a strided array ignoring NaN values and using a one-pass textbook algorithm.
• nanvariancewd( N, correction, x, strideX ): calculate the variance of a strided array ignoring NaN values and using Welford's algorithm.
• nanvarianceyc( N, correction, x, strideX ): calculate the variance of a strided array ignoring NaN values and using a one-pass algorithm proposed by Youngs and Cramer.
• ndarray: base ndarray statistical functions.
• 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.
• 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.
• smean( N, x, strideX ): 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.
• 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, strideX ): calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm.
• snanmean( N, x, stride ): calculate the arithmetic mean 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.
• 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.
• sstdev( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array.
• sstdevwd( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array using Welford's algorithm.
• 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.
• svariancewd( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array using Welford's algorithm.
• 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, strideX ): 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 ) );