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: 2 months 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 (about 9 years ago)
- Default Branch: master
- Last Pushed: 2022-08-17T23:54:24.000Z (almost 4 years ago)
- Last Synced: 2024-12-03T23:47:58.458Z (over 1 year 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
Awesome Lists containing this project
README
# js-stats
Package provides the implementation of various statistics distribution such as normal distribution, fisher, student-t, and so on
[](https://travis-ci.org/chen0040/js-stats) [](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