StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics https://doi.org/10.1109/TVCG.2020.3030352
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StackGenVis/frontend/node_modules/gamma/readme.markdown

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# gamma
[gamma function](http://en.wikipedia.org/wiki/Gamma_function)
in javascript using the
[lanczos approximation](http://en.wikipedia.org/wiki/Lanczos_approximation)
for small values and the
[spouge approximation](https://en.wikipedia.org/wiki/Spouge's_approximation) for
larger values
[![browser support](http://ci.testling.com/substack/gamma.js.png)](http://ci.testling.com/substack/gamma.js)
[![build status](https://secure.travis-ci.org/substack/gamma.js.png)](http://travis-ci.org/substack/gamma.js)
# example
```
> var gamma = require('gamma')
> gamma(5)
23.999999999999996
> gamma(1.6)
0.8935153492876909
```
# methods
var gamma = require('gamma')
## gamma(z)
Return the gamma function over `z`. Complex numbers aren't supported, only reals.
## gamma.log(z)
Return the natural log of the gamma function for `z`.
This function is used internally by the spouge approximation to compute large
values.
# install
With [npm](http://npmjs.org) do:
```
npm install gamma
```
# kudos
Implementation transliterated from the python script on the wikipedia entry for
the
[lanczos approximation](http://en.wikipedia.org/wiki/Lanczos_approximation).
Spouge approximation from [Niggler](https://github.com/Niggler).
# license
MIT