https://github.com/chen0040/js-stats

Package provides the javascript implementation of various statistics and distribution
https://github.com/chen0040/js-stats

chi-square f-distribution normal-distribution statistics t-distribution

Last synced: 10 days ago
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Package provides the javascript implementation of various statistics and distribution

• Host: GitHub
• URL: https://github.com/chen0040/js-stats
• Owner: chen0040
• License: mit
• Created: 2017-05-16T04:13:44.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-08-17T23:54:24.000Z (over 2 years ago)
• Last Synced: 2024-12-03T23:47:58.458Z (22 days ago)
• Topics: chi-square, f-distribution, normal-distribution, statistics, t-distribution
• Language: JavaScript
• Homepage:
• Size: 32.2 KB
• Stars: 9
• Watchers: 1
• Forks: 4
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# js-stats

Package provides the implementation of various statistics distribution such as normal distribution, fisher, student-t, and so on

[![Build Status](https://travis-ci.org/chen0040/js-stats.svg?branch=master)](https://travis-ci.org/chen0040/js-stats) [![Coverage Status](https://coveralls.io/repos/github/chen0040/js-stats/badge.svg?branch=master)](https://coveralls.io/github/chen0040/js-stats?branch=master) 

# Features

* Normal Distribution

  

  - cumulativeProbability(Z)

  - invCumulativeProbability(p)

* Student's T Distribution

  - cumulativeProbability(t_df)

  - invCumulativeProbability(p)

* Fisher–Snedecor Distribution

  - cumulativeProbabiliyt(F)

* Chi-Square Distribution

  - cumulativeProbabiliy(ChiSquare)

# Install

Run the following npm command to install

```bash

npm install js-stats

```

# Usage

Sample code is available at [playground](https://runkit.com/chen0040/js-stats-playground)

### Using with nodejs

```javascript

jsstats = require('js-stats');

//====================NORMAL DISTRIBUTION====================//

var mu = 0.0; // mean

var sd = 1.0; // standard deviation

var normal_distribution = new jsstats.NormalDistribution(mu, sd);

var X = 10.0; // point estimate value 

var p = normal_distribution.cumulativeProbability(X); // cumulative probability

var p = 0.7; // cumulative probability

var X = normal_distribution.invCumulativeProbability(p); // point estimate value

//====================T DISTRIBUTION====================//

var df = 10; // degrees of freedom for t-distribution

var t_distribution = new jsstats.TDistribution(df);

var t_df = 10.0; // point estimate or test statistic

var p = t_distribution.cumulativeProbability(t_df); // cumulative probability

var p = 0.7;

var t_df = t_distribution.invCumulativeProbability(p); // point estimate or test statistic

//====================F DISTRIBUTION====================//

var df1 = 10; // degrees of freedom for f-distribution

var df2 = 20; // degrees of freedom for f-distribution

var f_distribution = new jsstats.FDistribution(df1, df2);

var F = 10.0; // point estimate or test statistic

var p = f_distribution.cumulativeProbability(F); // cumulative probability

//====================Chi Square DISTRIBUTION====================//

var df = 10; // degrees of freedom for cs-distribution

var cs_distribution = new jsstats.ChiSquareDistribution(df);

var X = 10.0; // point estimate or test statistic

var p = cs_distribution.cumulativeProbability(X); // cumulative probability

```

### Using with HTML page