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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Chi-squared distribution.
To use in Observable,
chisquare = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-chisquare@umd/browser.js' )To vendor stdlib functionality and avoid installing dependency trees for Node.js, you can use the UMD server build:
var chisquare = require( 'path/to/vendor/umd/stats-base-dists-chisquare/index.js' )To include the bundle in a webpage,
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-chisquare@umd/browser.js"></script>If no recognized module system is present, access bundle contents via the global scope:
<script type="text/javascript">
(function () {
window.chisquare;
})();
</script>Chi-squared distribution.
var dist = chisquare;
// returns {...}The namespace contains the following distribution functions:
cdf( x, k ): Chi-squared distribution cumulative distribution function.logpdf( x, k ): evaluate the natural logarithm of the probability density function (PDF) for a chi-squared distribution.mgf( t, k ): Chi-squared distribution moment-generating function (MGF).pdf( x, k ): Chi-squared distribution probability density function (PDF).quantile( p, k ): Chi-squared distribution quantile function.
The namespace contains the following functions for calculating distribution properties:
entropy( k ): Chi-squared distribution differential entropy.kurtosis( k ): Chi-squared distribution excess kurtosis.mean( k ): Chi-squared distribution expected value.median( k ): Chi-squared distribution median.mode( k ): Chi-squared distribution mode.skewness( k ): Chi-squared distribution skewness.stdev( k ): Chi-squared distribution standard deviation.variance( k ): Chi-squared distribution variance.
The namespace contains a constructor function for creating a Chi-squared distribution object.
ChiSquare( [k] ): Chi-squared distribution constructor.
var ChiSquare = require( '@stdlib/stats-base-dists-chisquare' ).ChiSquare;
var dist = new ChiSquare( 4.0 );
var mu = dist.mean;
// returns 4.0<!DOCTYPE html>
<html lang="en">
<body>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/math-base-special-roundn@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-chisquare@umd/browser.js"></script>
<script type="text/javascript">
(function () {
// Define degrees of freedom:
var k = 5.0;
// Calculate distribution properties:
console.log( 'Mean: %d', chisquare.mean( k ) );
console.log( 'Median: %d', roundn( chisquare.median( k ), -4 ) );
console.log( 'Mode: %d', chisquare.mode( k ) );
console.log( 'Variance: %d', chisquare.variance( k ) );
console.log( 'Standard Deviation: %d', roundn( chisquare.stdev( k ), -4 ) );
console.log( 'Skewness: %d', roundn( chisquare.skewness( k ), -4 ) );
console.log( 'Excess Kurtosis: %d', roundn( chisquare.kurtosis( k ), -4 ) );
console.log( 'Entropy: %d', roundn( chisquare.entropy( k ), -4 ) );
// Evaluate probability functions:
var x = 3.0;
console.log( '\nEvaluating at x = %d', x );
console.log( 'PDF: %d', roundn( chisquare.pdf( x, k ), -4 ) );
console.log( 'logPDF: %d', roundn( chisquare.logpdf( x, k ), -4 ) );
console.log( 'CDF: %d', roundn( chisquare.cdf( x, k ), -4 ) );
// Calculate quantiles:
var p = 0.7;
console.log( '\nQuantile at p = %d: %d', p, roundn( chisquare.quantile( p, k ), -4 ) );
// Evaluate moment-generating function:
var t = 0.1;
console.log( 'MGF at t = %d: %d', t, roundn( chisquare.mgf( t, k ), -4 ) );
})();
</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.