simple-statistics

What is simple-statistics?

The simple-statistics npm package provides a comprehensive set of statistical tools and functions for performing various statistical analyses. It is designed to be easy to use and covers a wide range of statistical operations, from basic descriptive statistics to more complex regression analysis.

What are simple-statistics's main functionalities?

Descriptive Statistics

This feature allows you to calculate basic descriptive statistics such as mean, median, and variance. The code sample demonstrates how to compute these statistics for a given dataset.

const ss = require('simple-statistics');
const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
const mean = ss.mean(data);
const median = ss.median(data);
const variance = ss.variance(data);
console.log(`Mean: ${mean}, Median: ${median}, Variance: ${variance}`);

Probability Distributions

This feature provides functions to work with various probability distributions, including normal and binomial distributions. The code sample shows how to generate a random normal value and a binomial distribution.

const ss = require('simple-statistics');
const normal = ss.randomNormal(0, 1);
const binomial = ss.binomialDistribution(10, 0.5);
console.log(`Random Normal Value: ${normal}, Binomial Distribution: ${binomial}`);

Regression Analysis

This feature allows you to perform regression analysis, including linear regression. The code sample demonstrates how to compute a linear regression line for a given dataset.

const ss = require('simple-statistics');
const data = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5]];
const regression = ss.linearRegression(data);
const regressionLine = ss.linearRegressionLine(regression);
console.log(`Regression Line: y = ${regression.m}x + ${regression.b}`);

Hypothesis Testing

This feature provides functions for performing hypothesis testing, such as t-tests. The code sample shows how to perform a two-sample t-test on two datasets.

const ss = require('simple-statistics');
const tTest = ss.tTestTwoSample([1, 2, 3, 4, 5], [6, 7, 8, 9, 10]);
console.log(`T-Test Result: ${tTest}`);

Other packages similar to simple-statistics

jstat

jstat is a JavaScript library for statistical operations. It offers a wide range of statistical functions similar to simple-statistics, including descriptive statistics, probability distributions, and hypothesis testing. However, jstat has a more extensive API and supports matrix operations, making it suitable for more complex statistical analyses.

mathjs

mathjs is a comprehensive mathematics library for JavaScript and Node.js. It includes a variety of mathematical functions, including statistical operations. While it is not solely focused on statistics like simple-statistics, it provides a broader range of mathematical tools, making it a versatile choice for projects that require both statistical and general mathematical computations.

ssjs

ssjs (Statistical Software in JavaScript) is another library that provides statistical functions for JavaScript. It offers similar functionalities to simple-statistics, such as descriptive statistics and probability distributions. However, ssjs is less popular and has a smaller community compared to simple-statistics, which may affect the availability of support and updates.

README

A project to learn about and make simple reference implementations of statistics algorithms.

This code is designed to work in browsers (including IE) as well as in node.js.

Basic Descriptive Statistics

// Require simple statistics
var ss = require('simple-statistics');

// The input is a simple array
var list = [1, 2, 3];

// Many different descriptive statistics are supported
var sum = ss.sum(list),
    mean = ss.mean(list),
    min = ss.min(list),
    geometric_mean = ss.geometric_mean(list),
    max = ss.max(list),
    quantile = ss.quantile(0.25);

Linear Regression

// For a linear regression, it's a two-dimensional array
var data = [ [1, 2], [2, 3] ];

// simple-statistics can produce a linear regression and return
// a friendly javascript function for the line.
var line = ss.linear_regression()
    .data(data)
    .line();

// get a point along the line function
line(0);

var line = ss.linear_regression()

// Get the r-squared value of the line estimation
ss.r_squared(data, line);

Literate Documentation

Mixin Style

This is optional and not used by default. You can opt-in to mixins with ss.mixin().

This mixes simple-statistics methods into the Array prototype - note that extending native objects is a tricky move.

This will only work if defineProperty is available, which means modern browsers and nodejs - on IE8 and below, calling ss.mixin() will throw an exception.

// mixin to Array class
ss.mixin();

// The input is a simple array
var list = [1, 2, 3];

// The same descriptive techniques as above, but in a simpler style
var sum = list.sum(),
    mean = list.mean(),
    min = list.min(),
    max = list.max(),
    quantile = list.quantile(0.25);

Bayesian Classifier

var bayes = ss.bayesian();
bayes.train({ species: 'Cat' }, 'animal');
bayes.score({ species: 'Cat' });
// { animal: 1 }

Examples

• Linear regression with simple-statistics and d3js
• Jenks Natural Breaks with a choropleth map with d3js

Usage

To use it in browsers, grab simple_statistics.js. To use it in node, install it with npm or add it to your package.json.

npm install simple-statistics

To use it with component,

component install tmcw/simple-statistics

Documentation

Tests

Contributors

• Tom MacWright
• Matt Sacks
• Doron Linder
• Alexander Sicular

See Also

• stream-statistics, a sister project that implements many of the same measures for streaming data - as online algorithms

Javascript

• science.js
• atoll.js
• descriptive_statistics
• jStat
• classifier is a naive bayesian classifier (though specialized for the words-spam case)
• underscore.math

Python

• Pandas
• SciPy

Their Own Language

• Julia Language
• R language

Changelog

0.4.0

• Adds simple_statistics.median_absolute_deviation() by siculars
• Adds simple_statistics.iqr() by siculars
• Adds simple_statistics.skewness() by Doron Linder
• Lower-level accessors for linear regression allow users to do the line equation themselves