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.
When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.
To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!
Negative binomial distribution.
import negativeBinomial from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-negative-binomial@deno/mod.js';You can also import the following named exports from the package:
import { NegativeBinomial, cdf, kurtosis, logpmf, mean, mgf, mode, pmf, quantile, skewness, stdev, variance } from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-negative-binomial@deno/mod.js';Negative binomial distribution.
var dist = negativeBinomial;
// returns {...}The namespace contains the following distribution functions:
cdf( x, r, p ): negative binomial distribution cumulative distribution function.logpmf( x, r, p ): evaluate the natural logarithm of the probability mass function (PMF) for a negative binomial distribution.mgf( t, r, p ): negative binomial distribution moment-generating function (MGF).pmf( x, r, p ): negative binomial distribution probability mass function (PMF).quantile( k, r, p ): negative binomial distribution quantile function.
The namespace contains the following functions for calculating distribution properties:
kurtosis( r, p ): negative binomial distribution excess kurtosis.mean( r, p ): negative binomial distribution expected value.mode( r, p ): negative binomial distribution mode.skewness( r, p ): negative binomial distribution skewness.stdev( r, p ): negative binomial distribution standard deviation.variance( r, p ): negative binomial distribution variance.
The namespace contains a constructor function for creating a negative binomial distribution object.
NegativeBinomial( [r, p] ): negative binomial distribution constructor.
var NegativeBinomial = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-negative-binomial' ).NegativeBinomial;
var dist = new NegativeBinomial( 4.0, 0.2 );
var y = dist.pmf( 4.0 );
// returns ~0.023import negativeBinomial from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-negative-binomial@deno/mod.js';
/*
* Let's take an example of flipping a biased coin until getting 5 heads.
* This situation can be modeled using a Negative Binomial distribution with r = 5 and p = 1/2.
*/
var r = 5.0;
var p = 1/2;
// Mean can be used to calculate the average number of trials needed to get 5 heads:
console.log( negativeBinomial.mean( r, p ) );
// => 5
// PMF can be used to calculate the probability of getting heads on a specific trial (say on the 8th trial):
console.log( negativeBinomial.pmf( 8, r, p ) );
// => ~0.06
// CDF can be used to calculate the probability up to a certain number of trials (say up to 8 trials):
console.log( negativeBinomial.cdf( 8, r, p ) );
// => ~0.867
// Quantile can be used to calculate the number of trials at which you can be 80% confident that the actual number will not exceed:
console.log( negativeBinomial.quantile( 0.8, r, p ) );
// => 7
// Standard deviation can be used to calculate the measure of the spread of trials around the mean:
console.log( negativeBinomial.stdev( r, p ) );
// => ~3.162
// Skewness can be used to calculate the asymmetry of the distribution of trials:
console.log( negativeBinomial.skewness( r, p ) );
// => ~0.949
// MGF can be used for more advanced statistical analyses and generating moments of the distribution:
console.log( negativeBinomial.mgf( 0.5, r, p ) );
// => ~2277.597This package is part of stdlib, a standard library 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.
Copyright © 2016-2024. The Stdlib Authors.