Standard library statistical functions.
var statistics = require( '@stdlib/stats' );Standard library 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.binomTest( x[, n][, opts] ): exact test for the success probability in a Bernoulli experiment.chi2gof( x, y[, ...args][, opts] ): perform a chi-square goodness-of-fit 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.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.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.
Other statistical functions included are:
kde2d(): two-dimensional kernel density estimation.lowess( x, y[, opts] ): locally-weighted polynomial regression via the LOWESS algorithm.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 ) );