statistics-skinny: A library of statistical types, data, and functions

[ bsd2, library, math, statistics ] [ Propose Tags ]

This library provides a number of common functions and types useful in statistics. We focus on high performance, numerical robustness, and use of good algorithms. Where possible, we provide references to the statistical literature.

The library's facilities can be divided into four broad categories:

  • Working with widely used discrete and continuous probability distributions. (There are dozens of exotic distributions in use; we focus on the most common.)

  • Computing with sample data: quantile estimation, kernel density estimation, histograms, bootstrap methods, significance testing, and regression and autocorrelation analysis.

  • Random variate generation under several different distributions.

  • Common statistical tests for significant differences between samples.


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Versions [RSS] 0.15.2.0
Change log changelog.md
Dependencies async (>=2.2.2 && <2.3), base (>=4.8 && <5), base-orphans (>=0.6 && <0.9), data-default-class (>=0.1.2), deepseq (>=1.1.0.2), dense-linear-algebra (>=0.1 && <0.2), ghc-prim, math-functions (>=0.3), monad-par (>=0.3.4), mwc-random (>=0.13.0.0), primitive (>=0.3), vector (>=0.10), vector-algorithms (>=0.4), vector-th-unbox [details]
License BSD-2-Clause
Copyright 2009-2014 Bryan O'Sullivan
Author Bryan O'Sullivan <bos@serpentine.com>, Alexey Khudaykov <alexey.skladnoy@gmail.com>
Maintainer Vanessa McHale <vamchale@gmail.com>
Category Math, Statistics
Home page https://github.com/bos/statistics
Bug tracker https://github.com/bos/statistics/issues
Source repo head: git clone https://github.com/vmchale/statistics
Uploaded by vmchale at 2021-01-11T23:29:57Z
Distributions NixOS:0.15.2.0
Downloads 129 total (0 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2021-01-12 [all 1 reports]

Readme for statistics-skinny-0.15.2.0

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Statistics: efficient, general purpose statistics

This package provides the Statistics module, a Haskell library for working with statistical data in a space- and time-efficient way.

Where possible, we give citations and computational complexity estimates for the algorithms used.

Performance

This library has been carefully optimised for high performance. To obtain the best runtime efficiency, it is imperative to compile libraries and applications that use this library using a high level of optimisation.

Get involved!

Please report bugs via the github issue tracker.

Master git mirror:

  • git clone git://github.com/bos/statistics.git

Authors

This library is written by Bryan O'Sullivan, bos@serpentine.com.