Statistical functions.
var statistics = require( '@stdlib/stats' );Namespace containing statistical functions.
var stats = statistics;
// returns {...}The namespace exposes the following statistical tests:
anova1( x, factor[, opts] ): perform a one-way analysis of variance.bartlettTest( a[,b,...,k][, opts] ): compute Bartlett’s test for equal variances.binomialTest( x[, n][, opts] ): exact test for the success probability in a Bernoulli experiment.chi2gof( x, y[, ...args][, options] ): perform a chi-square goodness-of-fit test.chi2test( x[, options] ): perform a chi-square independence test.flignerTest( a[,b,...,k][, opts] ): compute the Fligner-Killeen test for equal variances.kruskalTest( a[,b,...,k][, opts] ): compute the Kruskal-Wallis test for equal medians.kstest( x, y[, ...params][, opts] ): one-sample Kolmogorov-Smirnov goodness-of-fit test.leveneTest( x[, y, ..., z][, opts] ): compute Levene's test for equal variances.pcorrtest( x, y[, opts] ): compute a Pearson product-moment correlation test between paired samples.ttest( x[, y][, opts] ): one-sample and paired Student's t-Test.ttest2( x, y[, opts] ): two-sample Student's t-Test.vartest( x, y[, opts] ): two-sample F-test for equal variances.wilcoxon( x[, y][, opts] ): one-sample and paired Wilcoxon signed rank test.ztest( x, sigma[, opts] ): one-sample z-Test.ztest2( x, y, sigmax, sigmay[, opts] ): two-sample z-Test.
In addition, it contains an assortment of functions for computing statistics incrementally as part of the incr sub-namespace and functions for computing statistics over iterators in the iterators namespace.
The namespace further contains functions for computing statistics on arrays as part of the array sub-namespace and functions for computing statistics on strided arrays in the strided namespace.
The base sub-namespace contains lower-level statistical functions, including a dists namespace containing functions related to a wide assortment of probability distributions.
base: base (i.e., lower-level) statistical functions.
Other statistical functions included are:
cumax( x[, options] ): compute the cumulative maximum value along one or more ndarray dimensions.cumin( x[, options] ): compute the cumulative minimum value along one or more ndarray dimensions.kde2d(): two-dimensional kernel density estimation.lowess( x, y[, opts] ): locally-weighted polynomial regression via the LOWESS algorithm.maxBy( x[, options], clbk[, thisArg] ): compute the maximum value along one or more ndarray dimensions according to a callback function.max( x[, options] ): compute the maximum value along one or more ndarray dimensions.maxabs( x[, options] ): compute the maximum absolute value along one or more ndarray dimensions.maxsorted( x[, options] ): compute the maximum value along one or more sorted ndarray dimensions.mean( x[, options] ): compute the arithmetic mean along one or more ndarray dimensions.meankbn( x[, options] ): compute the arithmetic mean along one or more ndarray dimensions using an improved Kahan–Babuška algorithm.meankbn2( x[, options] ): compute the arithmetic mean along one or more ndarray dimensions using a second-order iterative Kahan–Babuška algorithm.meanors( x[, options] ): compute the arithmetic mean along one or more ndarray dimensions using ordinary recursive summation.meanpn( x[, options] ): compute the arithmetic mean along one or more ndarray dimensions using a two-pass error correction algorithm.meanpw( x[, options] ): compute the arithmetic mean along one or more ndarray dimensions using pairwise summation.meanwd( x[, options] ): compute the arithmetic mean along one or more ndarray dimensions using Welford's algorithm.mediansorted( x[, options] ): compute the median value along one or more sorted ndarray dimensions.midrangeBy( x[, options], clbk[, thisArg] ): compute the mid-range along one or more ndarray dimensions according to a callback function.midrange( x[, options] ): compute the mid-range along one or more ndarray dimensions.minBy( x[, options], clbk[, thisArg] ): compute the minimum value along one or more ndarray dimensions according to a callback function.min( x[, options] ): compute the minimum value along one or more ndarray dimensions.minabs( x[, options] ): compute the minimum absolute value along one or more ndarray dimensions.minsorted( x[, options] ): compute the minimum value along one or more sorted ndarray dimensions.nanmaxBy( x[, options], clbk[, thisArg] ): compute the maximum value along one or more ndarray dimensions according to a callback function, ignoringNaNvalues.nanmax( x[, options] ): compute the maximum value along one or more ndarray dimensions, ignoringNaNvalues.nanmaxabs( x[, options] ): compute the maximum absolute value along one or more ndarray dimensions, ignoringNaNvalues.nanmean( x[, options] ): compute the arithmetic mean along one or more ndarray dimensions, ignoringNaNvalues.nanmeanors( x[, options] ): compute the arithmetic mean along one or more ndarray dimensions, ignoringNaNvalues and using ordinary recursive summation.nanmeanpn( x[, options] ): compute the arithmetic mean along one or more ndarray dimensions, ignoringNaNvalues and using a two-pass error correction algorithm.nanmeanwd( x[, options] ): compute the arithmetic mean along one or more ndarray dimensions, ignoringNaNvalues and using Welford's algorithm.nanmidrangeBy( x[, options], clbk[, thisArg] ): compute the mid-range along one or more ndarray dimensions according to a callback function, ignoringNaNvalues.nanminBy( x[, options], clbk[, thisArg] ): compute the minimum value along one or more ndarray dimensions according to a callback function, ignoringNaNvalues.nanmin( x[, options] ): compute the minimum value along one or more ndarray dimensions, ignoringNaNvalues.nanminabs( x[, options] ): compute the minimum absolute value along one or more ndarray dimensions, ignoringNaNvalues.nanrangeBy( x[, options], clbk[, thisArg] ): compute the range along one or more ndarray dimensions according to a callback function, ignoringNaNvalues.nanrange( x[, options] ): compute the range along one or more ndarray dimensions, ignoringNaNvalues.padjust( pvals, method[, comparisons] ): adjust supplied p-values for multiple comparisons.rangeBy( x[, options], clbk[, thisArg] ): compute the range along one or more ndarray dimensions according to a callback function.range( x[, options] ): compute the range along one or more ndarray dimensions.rangeabs( x[, options] ): compute the range of absolute values along one or more ndarray dimensions.ranks( arr[, opts] ): compute ranks for values of an array-like object.
var objectKeys = require( '@stdlib/utils/keys' );
var statistics = require( '@stdlib/stats' );
console.log( objectKeys( statistics ) );