README.md

About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

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Stats

Statistical functions.

Installation

npm install @stdlib/stats

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var statistics = require( '@stdlib/stats' );

statistics

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.

• incr: incremental statistics.
• iterators: statistical function iterators.

The base sub-namespace contains functions to calculate statistics alongside 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:

• kde2d(): two-dimensional kernel density estimation.
• lowess( x, y[, opts] ): locally-weighted polynomial regression via the LOWESS algorithm.
• padjust( pvals, method[, comparisons] ): adjust supplied p-values for multiple comparisons.
• ranks( arr[, opts] ): compute ranks for values of an array-like object.

Examples

var objectKeys = require( '@stdlib/utils/keys' );
var statistics = require( '@stdlib/stats' );

console.log( objectKeys( statistics ) );

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Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

Community

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.