The statistics package

[Tags: bsd3, library]

This library provides a number of common functions and types useful in statistics. Our focus is 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, bootstrap methods, signigicance testing, and autocorrelation analysis.

Random variate generation under several different distributions.

Common statistical tests for significant differences between samples.

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Versions0.1, 0.2, 0.2.1, 0.2.2, 0.3, 0.3.1, 0.3.2, 0.3.3, 0.3.4, 0.3.5, 0.3.6, 0.4.0, 0.4.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
Change logNone available
Dependenciesbase (<4.8), erf, mwc-random (>=, primitive (>=0.3), time, vector (>=, vector-algorithms (>=0.4) [details]
Copyright2009, 2010 Bryan O'Sullivan
AuthorBryan O'Sullivan <>
MaintainerBryan O'Sullivan <>
CategoryMath, Statistics
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Source repositoryhead: hg clone
UploadedMon Nov 22 23:04:47 UTC 2010 by BryanOSullivan
UpdatedFri Jan 9 11:33:28 UTC 2015 by HerbertValerioRiedel to revision 1
DistributionsDebian:, FreeBSD:, LTSHaskell:, NixOS:, Stackage:
Downloads25222 total (513 in last 30 days)
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Maintainers' corner

For package maintainers and hackage trustees

Readme for statistics-

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.


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.

Suggested GHC options:

-O -funbox-strict-fields

To illustrate, here are the times (in seconds) to generate and sum 250 million random Word32 values, on a laptop with a 2.4GHz Core2 Duo P8600 processor, running Fedora 11 and GHC 6.10.3:

no flags   200+
-O           1.249
-O -fvia-C   0.991

As the numbers above suggest, compiling without optimisation will yield unacceptable performance.

Get involved!

Please report bugs via the bitbucket issue tracker.

Master Mercurial repository:

There's also a git mirror:

(You can create and contribute changes using either Mercurial or git.)


This library is written and maintained by Bryan O'Sullivan,