Base (i.e., lower-level) statistical functions.
var stats = require( '@stdlib/stats/base' );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.nanmskmax( N, x, strideX, mask, strideMask ): calculate the maximum value of a strided array according to a mask, ignoringNaNvalues.nanmskmin( N, x, strideX, mask, strideMask ): calculate the minimum value of a strided array according to a mask, ignoringNaNvalues.nanmskrange( N, x, strideX, mask, strideMask ): calculate the range of a strided array according to a mask, ignoringNaNvalues.nanrangeBy( N, x, stride, clbk[, thisArg] ): calculate the range of a strided array via a callback function, ignoringNaNvalues.nanrange( N, x, strideX ): calculate the range of a strided array, ignoringNaNvalues.nanstdev( N, correction, x, strideX ): calculate the standard deviation of a strided array ignoringNaNvalues.nanstdevch( N, correction, x, stride ): calculate the standard deviation of a strided array ignoringNaNvalues and using a one-pass trial mean algorithm.nanstdevpn( N, correction, x, strideX ): calculate the standard deviation of a strided array ignoringNaNvalues and using a two-pass algorithm.nanstdevtk( N, correction, x, stride ): calculate the standard deviation of a strided array ignoringNaNvalues and using a one-pass textbook algorithm.nanstdevwd( N, correction, x, stride ): calculate the standard deviation of a strided array ignoringNaNvalues and using Welford's algorithm.nanstdevyc( N, correction, x, strideX ): calculate the standard deviation of a strided array ignoringNaNvalues and using a one-pass algorithm proposed by Youngs and Cramer.nanvariance( N, correction, x, strideX ): calculate the variance of a strided array ignoringNaNvalues.nanvariancech( N, correction, x, strideX ): calculate the variance of a strided array ignoringNaNvalues and using a one-pass trial mean algorithm.nanvariancepn( N, correction, x, strideX ): calculate the variance of a strided array ignoringNaNvalues and using a two-pass algorithm.nanvariancetk( N, correction, x, strideX ): calculate the variance of a strided array ignoringNaNvalues and using a one-pass textbook algorithm.nanvariancewd( N, correction, x, strideX ): calculate the variance of a strided array ignoringNaNvalues and using Welford's algorithm.nanvarianceyc( N, correction, x, strideX ): calculate the variance of a strided array ignoringNaNvalues 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, strideX ): calculate the range of a strided array.sdsnanmean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoringNaNvalues and using extended accumulation.sdsnanmeanors( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoringNaNvalues and using ordinary recursive summation with extended accumulation.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.snanmean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoringNaNvalues.snanstdev( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoringNaNvalues.snanstdevch( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoringNaNvalues 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 ignoringNaNvalues and using a two-pass algorithm.snanstdevtk( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoringNaNvalues and using a one-pass textbook algorithm.snanstdevwd( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoringNaNvalues and using Welford's algorithm.snanstdevyc( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoringNaNvalues 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 ignoringNaNvalues.snanvariancech( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoringNaNvalues and using a one-pass trial mean algorithm.snanvariancepn( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoringNaNvalues and using a two-pass algorithm.snanvariancetk( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoringNaNvalues and using a one-pass textbook algorithm.snanvariancewd( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoringNaNvalues and using Welford's algorithm.snanvarianceyc( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoringNaNvalues and using a one-pass algorithm proposed by Youngs and Cramer.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, strideX ): calculate the standard deviation of a strided array.stdevch( N, correction, x, strideX ): calculate the standard deviation of a strided array using a one-pass trial mean algorithm.stdevpn( N, correction, x, strideX ): 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.variance( N, correction, x, strideX ): calculate the variance of a strided array.variancech( N, correction, x, strideX ): calculate the variance of a strided array using a one-pass trial mean algorithm.variancepn( N, correction, x, strideX ): calculate the variance of a strided array using a two-pass algorithm.variancetk( N, correction, x, strideX ): 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.
var objectKeys = require( '@stdlib/utils/keys' );
var ns = require( '@stdlib/stats/base' );
console.log( objectKeys( ns ) );