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.
The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.
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Statistical functions.
To use in Observable,
statistics = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/stats@umd/browser.js' )To vendor stdlib functionality and avoid installing dependency trees for Node.js, you can use the UMD server build:
var statistics = require( 'path/to/vendor/umd/stats/index.js' )To include the bundle in a webpage,
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats@umd/browser.js"></script>If no recognized module system is present, access bundle contents via the global scope:
<script type="text/javascript">
(function () {
window.statistics;
})();
</script>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 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.
<!DOCTYPE html>
<html lang="en">
<body>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/utils/keys@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats@umd/browser.js"></script>
<script type="text/javascript">
(function () {
console.log( objectKeys( statistics ) );
})();
</script>
</body>
</html>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.
See LICENSE.
Copyright © 2016-2024. The Stdlib Authors.