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@stdlib/stats-base-dists-binomial

Binomial distribution.

https://github.com/stdlib-js/stats-base-dists-binomial

Science Score: 44.0%

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Keywords

binom binomial discrete dist distribution javascript lib library node node-js nodejs prob probability standard statistics stats stdlib univariate
Last synced: 6 months ago · JSON representation ·

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Binomial distribution.

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Statistics
  • Stars: 2
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Topics
binom binomial discrete dist distribution javascript lib library node node-js nodejs prob probability standard statistics stats stdlib univariate
Created over 4 years ago · Last pushed 11 months ago
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Readme Changelog Contributing License Code of conduct Citation Security

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.

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!

Binomial

NPM version Build Status Coverage Status <!-- dependencies -->

Binomial distribution.

## Installation ```bash npm install @stdlib/stats-base-dists-binomial ``` Alternatively, - To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]). - If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions). - For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]). The [branches.md][branches-url] 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 ```javascript var binomial = require( '@stdlib/stats-base-dists-binomial' ); ``` #### binomial Binomial distribution. ```javascript var dist = binomial; // returns {...} ``` The namespace contains the following distribution functions:
- [`cdf( x, n, p )`][@stdlib/stats/base/dists/binomial/cdf]: binomial distribution cumulative distribution function. - [`logpmf( x, n, p )`][@stdlib/stats/base/dists/binomial/logpmf]: evaluate the natural logarithm of the probability mass function (PMF) for a binomial distribution. - [`mgf( t, n, p )`][@stdlib/stats/base/dists/binomial/mgf]: binomial distribution moment-generating function (MGF). - [`pmf( x, n, p )`][@stdlib/stats/base/dists/binomial/pmf]: binomial distribution probability mass function (PMF). - [`quantile( r, n, p )`][@stdlib/stats/base/dists/binomial/quantile]: binomial distribution quantile function.
The namespace contains the following functions for calculating distribution properties:
- [`entropy( n, p )`][@stdlib/stats/base/dists/binomial/entropy]: binomial distribution entropy. - [`kurtosis( n, p )`][@stdlib/stats/base/dists/binomial/kurtosis]: binomial distribution excess kurtosis. - [`mean( n, p )`][@stdlib/stats/base/dists/binomial/mean]: binomial distribution expected value. - [`median( n, p )`][@stdlib/stats/base/dists/binomial/median]: binomial distribution median. - [`mode( n, p )`][@stdlib/stats/base/dists/binomial/mode]: binomial distribution mode. - [`skewness( n, p )`][@stdlib/stats/base/dists/binomial/skewness]: binomial distribution skewness. - [`stdev( n, p )`][@stdlib/stats/base/dists/binomial/stdev]: binomial distribution standard deviation. - [`variance( n, p )`][@stdlib/stats/base/dists/binomial/variance]: binomial distribution variance.
The namespace contains a constructor function for creating a [binomial][binomial-distribution] distribution object.
- [`Binomial( [n, p] )`][@stdlib/stats/base/dists/binomial/ctor]: binomial distribution constructor.
```javascript var Binomial = require( '@stdlib/stats-base-dists-binomial' ).Binomial; var dist = new Binomial( 10, 0.4 ); var mu = dist.mean; // returns 4 ```
## Examples ```javascript var binomial = require( '@stdlib/stats-base-dists-binomial' ); /* * Let's take an example of rolling a fair dice 10 times and counting the number of times a 6 is rolled. * This situation can be modeled using a Binomial distribution with n = 10 and p = 1/6 */ var n = 10; var p = 1/6; // Mean can be used to calculate the average number of times a 6 is rolled: console.log( binomial.mean( n, p ) ); // => ~1.6667 // PMF can be used to calculate the probability of getting a certain number of 6s (say 3 sixes): console.log( binomial.pmf( 3, n, p ) ); // => ~0.1550 // CDF can be used to calculate probability up to certain number of 6s (say up to 3 sixes): console.log( binomial.cdf( 3, n, p ) ); // => ~0.9303 // Quantile can be used to calculate the number of 6s at which you can be 80% confident that the actual number will not exceed. console.log( binomial.quantile( 0.8, n, p ) ); // => 3 // Standard deviation can be used to calculate the measure of the spread of 6s around the mean: console.log( binomial.stdev( n, p ) ); // => ~1.1785 // Skewness can be used to calculate the asymmetry of the distribution of 6s: console.log( binomial.skewness( n, p ) ); // => ~0.5657 // MGF can be used for more advanced statistical analyses and generating moments of the distribution: console.log( binomial.mgf( 0.5, n, p ) ); // => ~2.7917 ```
* * * ## Notice This package is part of [stdlib][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][stdlib], see the main project [repository][stdlib]. #### Community [![Chat][chat-image]][chat-url] --- ## License See [LICENSE][stdlib-license]. ## Copyright Copyright © 2016-2025. The Stdlib [Authors][stdlib-authors].

Owner

  • Name: stdlib
  • Login: stdlib-js
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Standard library for JavaScript.

Citation (CITATION.cff) 

cff-version: 1.2.0
title: stdlib
message: >-
  If you use this software, please cite it using the
  metadata from this file.

type: software

authors:
  - name: The Stdlib Authors
    url: https://github.com/stdlib-js/stdlib/graphs/contributors

repository-code: https://github.com/stdlib-js/stdlib
url: https://stdlib.io

abstract: |
  Standard library for JavaScript and Node.js.

keywords:
  - JavaScript
  - Node.js
  - TypeScript
  - standard library
  - scientific computing
  - numerical computing
  - statistical computing

license: Apache-2.0 AND BSL-1.0

date-released: 2016

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  • Total versions: 11
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npmjs.org: @stdlib/stats-base-dists-binomial

Binomial distribution.

  • Homepage: https://stdlib.io
  • License: Apache-2.0
  • Latest release: 0.2.2
    published over 1 year ago
  • Versions: 11
  • Dependent Packages: 4
  • Dependent Repositories: 2
  • Downloads: 557 Last month
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Dependent packages count: 4.5%
Dependent repos count: 7.6%
Downloads: 8.6%
Average: 10.1%
Stargazers count: 14.5%
Forks count: 15.4%
Maintainers (4)
planeshifter rreusser stdlib-bot kgryte
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  • url: https://opencollective.com/stdlib
Last synced: 6 months ago

Dependencies

package.json npm
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  • @stdlib/stats-base-dists-binomial-logpmf ^0.0.x
  • @stdlib/stats-base-dists-binomial-mean ^0.0.x
  • @stdlib/stats-base-dists-binomial-median ^0.0.x
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  • @stdlib/stats-base-dists-binomial-skewness ^0.0.x
  • @stdlib/stats-base-dists-binomial-stdev ^0.0.x
  • @stdlib/stats-base-dists-binomial-variance ^0.0.x
  • @stdlib/utils-define-read-only-property ^0.0.x
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