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wink-statistics v2.0.0

Fast and Numerically Stable Statistical Analysis Utilities

wink-statistics

Fast and Numerically Stable Statistical Analysis Utilities

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Perform fast and numerically stable statistical analysis using wink-statistics. Apart from arrays, 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.

Installation

Use npm 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:

// 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:

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:

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 to learn more.

Need Help?

If you spot a bug and the same has not yet been reported, raise a new issue or consider fixing it and sending a pull request.

About wink

Wink 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 2017-18 GRAYPE Systems Private Limited.

It is licensed under the terms of the MIT License.

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