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https://github.com/synesenom/ran
Library for generating various random variables
https://github.com/synesenom/ran
distribution javascript mcmc random statistics test
Last synced: about 2 months ago
JSON representation
Library for generating various random variables
- Host: GitHub
- URL: https://github.com/synesenom/ran
- Owner: synesenom
- License: mit
- Created: 2017-07-05T06:25:29.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-10-06T12:22:52.000Z (about 1 year ago)
- Last Synced: 2024-04-14T08:33:41.947Z (9 months ago)
- Topics: distribution, javascript, mcmc, random, statistics, test
- Language: JavaScript
- Homepage: https://synesenom.github.io/ran/
- Size: 27.3 MB
- Stars: 13
- Watchers: 4
- Forks: 4
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://app.circleci.com/pipelines/github/synesenom/ran)
[](https://coveralls.io/github/synesenom/ran?branch=master)
[](https://www.npmjs.com/package/ranjs)
[](http://inch-ci.org/github/synesenom/ran)
[](https://www.npmjs.com/package/ranjs)
[](https://standardjs.com)
[](https://codescene.io/projects/28921)
# ranjs
Statistical library for generating various seeded random variates, calculating likelihood functions and testing hypotheses (and much more).
The library includes:
1. Statistical metrics and tests: a variety of central tendency, dispersion and shape statistics as well as statistical tests.
2. Probability distributions: more than [130 continuous and discrete distributions](https://synesenom.github.io/ran/#dist.Distribution) (and counting), each tested rigorously for statistical correctness over a variety of parameters. Every distribution comes with the following methods:
2.1 fast and robust sampler.
2.2 probability density/mass function.
2.3 cumulative distribution function.
2.4 quantile function.
2.5 survival, hazard and cumulative hazard functions.
2.6 likelihood and AIC/BIC methods.
2.7 test method that uses Kolmogorov-Smirnov test for continuous or chi2 tests for discrete distributions.
Also, every distribution can be individually seeded.
## install
### browser
Just include the [minified version](https://unpkg.com/[email protected]/dist/ranjs.min.js) and add
```
```
The module will be exported under `ranjs`.
### node
```
npm install ranjs
```
## usage
### distributions
```
const ran = require('ranjs')
// Create a new generator for Skellam distribution with mu1 = 1 and mu2 = 3
const skellam = new ran.dist.Skellam(1, 3)
// Generate 10K variates
let values = skellam.sample(1e4)
// Test if samples indeed follow the specified distribution
console.log(skellam.test(values))
// => { statistics: 14.025360669436635, passed: true }
// Evaluate PMF/CDF ...
for (let k = -10; k <= 10; k++) {
console.log(k, skellam.pdf(k), skellam.cdf(k))
}
// => -4 0.10963424740027695 0.21542206959904264
// -3 0.1662284357019246 0.38165050508716936
// -2 0.20277318483535026 0.5844236896611729
// ...
// ... or higher level statistical functions
for (let k = -4; k <= 4; k++) {
console.log(k, skellam.hazard(k), skellam.cHazard(k))
}
// => -4 0.13973659359019766 0.24260937407418487
// -3 0.26882602325948046 0.4807014556249526
// -2 0.487932492278074 0.8780890224913454
// ...
// Create another distribution and check their AIC
const skellam2 = new ran.dist.Skellam(1.2, 7.5)
console.log(`Skellam(1, 3): ${skellam.aic(values)}`)
// => Skellam(1, 3): 41937.67252974663
console.log(`Skellam(1.2, 7.5): ${skellam2.aic(values)}`)
// => Skellam(1.2, 7.5): 66508.74299363888
```
## demo
A demo observable notebook is available [here](https://beta.observablehq.com/@synesenom/ranjs-demo) to play around with the library.
## API and documentation
For the full API and documentation, see: [https://synesenom.github.io/ran/](https://synesenom.github.io/ran/)