# wink-statistics

Fast and Numerically Stable Statistical Analysis Utilities

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.