Statistical functions for arrays.
var ns = require( '@stdlib/stats/array' );Namespace containing APIs for performing statistical operations on arrays.
var o = ns;
// returns {...}The namespace exports the following:
maxBy( x, clbk[, thisArg] ): calculate the maximum value of an array via a callback function.max( x ): calculate the maximum value of an array.maxabs( x ): calculate the maximum absolute value of an array.maxsorted( x ): calculate the maximum value of a sorted array.mean( x ): calculate the arithmetic mean of an array.meankbn( x ): calculate the arithmetic mean of an array using an improved Kahan–Babuška algorithm.meankbn2( x ): calculate the arithmetic mean of an array using a second-order iterative Kahan–Babuška algorithm.meanors( x ): calculate the arithmetic mean of an array using ordinary recursive summation.meanpn( x ): calculate the arithmetic mean of an array using a two-pass error correction algorithm.meanpw( x ): calculate the arithmetic mean of an array using pairwise summation.meanwd( x ): calculate the arithmetic mean of an array using Welford's algorithm.mediansorted( x ): calculate the median value of a sorted array.minBy( x, clbk[, thisArg] ): calculate the minimum value of an array via a callback function.min( x ): calculate the minimum value of an array.minabs( x ): calculate the minimum absolute value of an array.minsorted( x ): calculate the minimum value of a sorted array.mskmax( x, mask ): calculate the maximum value of an array according to a mask.mskmin( x, mask ): calculate the minimum value of an array according to a mask.mskrange( x, mask ): calculate the range of an array according to a mask.nanmaxBy( x, clbk[, thisArg] ): calculate the maximum value of an array via a callback function, ignoringNaNvalues.nanmax( x ): calculate the maximum value of an array, ignoringNaNvalues.nanmaxabs( x ): calculate the maximum absolute value of an array, ignoringNaNvalues.nanmean( x ): calculate the arithmetic mean of an array, ignoringNaNvalues.nanmeanors( x ): calculate the arithmetic mean of an array, ignoringNaNvalues and using ordinary recursive summation.nanmeanpn( x ): calculate the arithmetic mean of an array, ignoringNaNvalues and using a two-pass error correction algorithm.nanmeanwd( x ): calculate the arithmetic mean of an array, ignoringNaNvalues and using Welford's algorithm.nanminBy( x, clbk[, thisArg] ): calculate the minimum value of an array via a callback function, ignoringNaNvalues.nanmin( x ): calculate the minimum value of an array, ignoringNaNvalues.nanminabs( x ): calculate the minimum absolute value of an array, ignoringNaNvalues.nanmskmax( x, mask ): calculate the maximum value of an array according to a mask, ignoringNaNvalues.nanmskmin( x, mask ): calculate the minimum value of an array according to a mask, ignoringNaNvalues.nanmskrange( x, mask ): calculate the range of an array according to a mask, ignoringNaNvalues.nanrangeBy( x, clbk[, thisArg] ): calculate the range of an array via a callback function, ignoringNaNvalues.nanrange( x ): calculate the range of an array, ignoringNaNvalues.nanstdev( x[, correction] ): calculate the standard deviation of an array ignoringNaNvalues.nanstdevch( x[, correction] ): calculate the standard deviation of an array ignoringNaNvalues and using a one-pass trial mean algorithm.nanstdevpn( x[, correction] ): calculate the standard deviation of an array ignoringNaNvalues and using a two-pass algorithm.nanstdevtk( x[, correction] ): calculate the standard deviation of an array ignoringNaNvalues and using a one-pass textbook algorithm.nanstdevwd( x[, correction] ): calculate the standard deviation of an array ignoringNaNvalues and using Welford's algorithm.nanstdevyc( x[, correction] ): calculate the standard deviation of an array ignoringNaNvalues and using a one-pass algorithm proposed by Youngs and Cramer.nanvariance( x[, correction] ): calculate the variance of an array ignoringNaNvalues.nanvariancech( x[, correction] ): calculate the variance of an array ignoringNaNvalues and using a one-pass trial mean algorithm.nanvariancepn( x[, correction] ): calculate the variance of an array ignoringNaNvalues and using a two-pass algorithm.nanvariancetk( x[, correction] ): calculate the variance of an array ignoringNaNvalues and using a one-pass textbook algorithm.nanvariancewd( x[, correction] ): calculate the variance of an array ignoringNaNvalues and using Welford's algorithm.nanvarianceyc( x[, correction] ): calculate the variance of an array ignoringNaNvalues and using a one-pass algorithm proposed by Youngs and Cramer.rangeBy( x, clbk[, thisArg] ): calculate the range of an array via a callback function.range( x ): calculate the range of an array.stdev( x[, correction] ): calculate the standard deviation of an array.stdevch( x[, correction] ): calculate the standard deviation of an array using a one-pass trial mean algorithm.stdevpn( x[, correction] ): calculate the standard deviation of an array using a two-pass algorithm.stdevtk( x[, correction] ): calculate the standard deviation of an array using a one-pass textbook algorithm.stdevwd( x[, correction] ): calculate the standard deviation of an array using Welford's algorithm.stdevyc( x[, correction] ): calculate the standard deviation of an array using a one-pass algorithm proposed by Youngs and Cramer.variance( x[, correction] ): calculate the variance of an array.variancech( x[, correction] ): calculate the variance of an array using a one-pass trial mean algorithm.variancepn( x[, correction] ): calculate the variance of an array using a two-pass algorithm.variancetk( x[, correction] ): calculate the variance of an array using a one-pass textbook algorithm.variancewd( x[, correction] ): calculate the variance of an array using Welford's algorithm.varianceyc( x[, correction] ): calculate the variance of an array using a one-pass algorithm proposed by Youngs and Cramer.
var objectKeys = require( '@stdlib/utils/keys' );
var ns = require( '@stdlib/stats/array' );
console.log( objectKeys( ns ) );