Open Source: Javascript implementation of Statistical Distribution

js-stats

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

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

 

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

npm install js-stats

Usage

Sample code is available at playground

Using with nodejs

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