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https://github.com/winkjs/wink-statistics
Fast & numerically stable statistical analysis
https://github.com/winkjs/wink-statistics
covariance hactoberfest probability real-time regression statistical-analysis statistics stats streaming
Last synced: 3 days ago
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Fast & numerically stable statistical analysis
- Host: GitHub
- URL: https://github.com/winkjs/wink-statistics
- Owner: winkjs
- License: mit
- Created: 2017-07-02T06:12:32.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-01-06T01:32:07.000Z (almost 2 years ago)
- Last Synced: 2024-10-28T05:12:24.705Z (15 days ago)
- Topics: covariance, hactoberfest, probability, real-time, regression, statistical-analysis, statistics, stats, streaming
- Language: JavaScript
- Homepage: http://winkjs.org/wink-statistics/
- Size: 2.21 MB
- Stars: 46
- Watchers: 8
- Forks: 7
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# wink-statistics
Fast and Numerically Stable Statistical Analysis Utilities
### [](https://travis-ci.org/winkjs/wink-statistics) [](https://coveralls.io/github/winkjs/wink-statistics?branch=master) [](https://inch-ci.org/github/winkjs/wink-statistics) [](https://david-dm.org/winkjs/wink-statistics) [](https://david-dm.org/winkjs/wink-statistics?type=dev) [](https://gitter.im/winkjs/Lobby)
[
](https://winkjs.org/)
Perform fast and numerically stable statistical analysis using **`wink-statistics`**. It can handle **real-time stream of data** and can incrementally compute required statistic that usually would take more than one pass over the data as in standard deviation or simple linear regression.
### Functions
1. [Boxplot](https://winkjs.org/wink-statistics/data.html#.boxplot)
1. [Covariance](https://winkjs.org/wink-statistics/streaming.html#covariance)
1. [Difference with definable lag](https://winkjs.org/wink-statistics/data.html#.difference)
1. [Five Number Summary](https://winkjs.org/wink-statistics/data.html#.fiveNumSummary)
1. [Frequency Table](https://winkjs.org/wink-statistics/streaming.html#freqTable)
1. [Histogram](https://winkjs.org/wink-statistics/data.html#.histogram)
1. [Median Absolute Deviation (MAD)](https://winkjs.org/wink-statistics/data.html#.mad)
1. [Maximum](https://winkjs.org/wink-statistics/streaming.html#max)
1. [Mean](https://winkjs.org/wink-statistics/streaming.html#mean)
1. [Median](https://winkjs.org/wink-statistics/data.html#.median)
1. [Minimum](https://winkjs.org/wink-statistics/streaming.html#min)
1. [Numerically stable sum](https://winkjs.org/wink-statistics/streaming.html#sum)
1. [Percentile](https://winkjs.org/wink-statistics/data.html#.percentile)
1. [Probability computation & CI from successes count](https://winkjs.org/wink-statistics/probability.html#.range4CI)
1. [Probability estimates aggregation](https://winkjs.org/wink-statistics/probability.html#.aggregate)
1. [Simple Linear Regression](https://winkjs.org/wink-statistics/streaming.html#simpleLinearRegression)
1. [Standard Deviation](https://winkjs.org/wink-statistics/streaming.html#stdev)
1. [Summary statistics](https://winkjs.org/wink-statistics/streaming.html#summary)
## Installation
Use [npm](https://www.npmjs.com/package/wink-statistics) to install:
npm install wink-statistics --save
## Getting Started
### Handling Streams
Here is an example of computing `slope`, `intercept` and `r2` etc. from a stream of `(x, y)` data in real-time:
```javascript
// Load wink-statistics.
var stats = require( 'wink-statistics' );
// Instantiate streaming simple linear regression
var regression = stats.streaming.simpleLinearRegression();
// Following would be ideally placed within a stream of data:
regression.compute( 10, 80 );
regression.compute( 15, 75 );
regression.compute( 16, 65 );
regression.compute( 18, 50 );
regression.compute( 21, 45 );
regression.compute( 30, 30 );
regression.compute( 36, 18 );
regression.compute( 40, 9 );
// Use result() method to access the outcome in real time.
regression.result();
// returns { slope: -2.3621,
// intercept: 101.4188,
// r: -0.9766,
// r2: 0.9537,
// se: 5.624,
// size: 8
// }
```
### Handling data array
The functions under the `data` name space require data in an array. Here is an example of boxplot analysis:
```javascript
var boxplot = stats.data.boxplot;
var data = [
-12, 14, 14, 14, 16, 18, 20, 20, 21, 23, 27, 27, 27, 29, 31,
31, 32, 32, 34, 36, 40, 40, 40, 40, 40, 42, 51, 56, 60, 88
];
boxplot( data );
// returns {
// min: -12, q1: 20, median: 31, q3: 40, max: 88,
// iqr: 20, range: 100, size: 30,
// leftOutliers: { begin: 0, end: 0, count: 1, fence: 14 },
// rightOutliers: { begin: 29, end: 29, count: 1, fence: 60 },
// leftNotch: 25.230655727612252,
// rightNotch: 36.76934427238775
// }
```
`wink-stats` can handle data in different formats to avoid pre-processing. For example, you can compute median from the array of objects containing value:
```javascript
var median = stats.data.median;
var data = [
{ value: 1 },
{ value: 1 },
{ value: 2 },
{ value: 2 },
{ value: 3 },
{ value: 3 },
{ value: 4 },
{ value: 4 }
];
// Use key name — `value` as the `accessor`
median( data, 'value' );
// returns 2.5
```
It even supports passing functions as `accessors` to handle even more complex data structures.
## Documentation
Check out the [statistics API documentation](https://winkjs.org/wink-statistics/) to learn more.
## Need Help?
If you spot a bug and the same has not yet been reported, raise a new [issue](https://github.com/winkjs/wink-statistics/issues) or consider fixing it and sending a pull request.
## About wink
[Wink](https://winkjs.org/) is a family of open source packages for **Statistical Analysis**, **Natural Language Processing** and **Machine Learning** in NodeJS. The code is **thoroughly documented** for easy human comprehension and has a **test coverage of ~100%** for reliability to build production grade solutions.
## Copyright & License
**wink-statistics** is copyright 2017-20 [GRAYPE Systems Private Limited](https://graype.in/).
It is licensed under the terms of the MIT License.