npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

regression

v2.0.1

Published

Javascript least squares data fitting methods

Downloads

616,266

Readme

Installation

This module works on node and in the browser. It is available as the 'regression' package on npm. It is also available on a CDN.

npm

npm install --save regression

Usage

import regression from 'regression';
const result = regression.linear([[0, 1], [32, 67], [12, 79]]);
const gradient = result.equation[0];
const yIntercept = result.equation[1];

Data is passed into the model as an array. A second parameter can be used to configure the model. The configuration parameter is optional. null values are ignored. The precision option will set the number of significant figures the output is rounded to.

Configuration options

Below are the default values for the configuration parameter.

{
  order: 2,
  precision: 2,
}

Properties

  • equation: an array containing the coefficients of the equation
  • string: A string representation of the equation
  • points: an array containing the predicted data in the domain of the input
  • r2: the coefficient of determination (R2)
  • predict(x): This function will return the predicted value

API

regression.linear(data[, options])

Fits the input data to a straight line with the equation y = mx + c. It returns the coefficients in the form [m, c].

regression.exponential(data[, options])

Fits the input data to a exponential curve with the equation y = ae^bx. It returns the coefficients in the form [a, b].

regression.logarithmic(data[, options])

Fits the input data to a logarithmic curve with the equation y = a + b ln x. It returns the coefficients in the form [a, b].

regression.power(data[, options])

Fits the input data to a power law curve with the equation y = ax^b. It returns the coefficients in the form [a, b].

regression.polynomial(data[, options])

Fits the input data to a polynomial curve with the equation anx^n ... + a1x + a0. It returns the coefficients in the form [an..., a1, a0]. The order can be configure with the order option.

Example

const data = [[0,1],[32, 67] .... [12, 79]];
const result = regression.polynomial(data, { order: 3 });

Development

  • Install the dependencies with npm install
  • To build the assets in the dist directory, use npm run build
  • You can run the tests with: npm run test.