## Summary

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 autocorrelation analysis. * Random variate generation under several different distributions. * Common statistical tests for significant differences between samples. Changes in 0.10.0.0: * The type classes @Mean@ and @Variance@ are split in two. This is required for distributions which do not have finite variance or mean. * The @S.Sample.KernelDensity@ module has been renamed, and completely rewritten to be much more robust. The older module oversmoothed multi-modal data. (The older module is still available under the name @S.Sample.KernelDensity.Simple@). * Histogram computation is added, in @S.Sample.Histogram@. * Forward and inverse discrete Fourier and cosine transforms are added, in @S.Transform@. * Root finding is added, in @S.Math.RootFinding@. * The @complCumulative@ function is added to the @Distribution@ class in order to accurately assess probalities P(X>x) which are used in one-tailed tests. * A @stdDev@ function is added to the @Variance@ class for distributions. * The constructor @S.Distribution.normalDistr@ now takes standard deviation instead of variance as its parameter. * A bug in @S.Quantile.weightedAvg@ is fixed. It produced a wrong answer if a sample contained only one element. * Bugs in quantile estimations for chi-square and gamma distribution are fixed. * Integer overlow in @mannWhitneyUCriticalValue@ is fixed. It produced incorrect critical values for moderately large samples. Something around 20 for 32-bit machines and 40 for 64-bit ones. * A bug in @mannWhitneyUSignificant@ is fixed. If either sample was larger than 20, it produced a completely incorrect answer. * One- and two-tailed tests in @S.Tests.NonParametric@ are selected with sum types instead of @Bool@. * Test results returned as enumeration instead of @Bool@. * Performance improvements for Mann-Whitney U and Wilcoxon tests. * Module @S.Tests.NonParamtric@ is split into @S.Tests.MannWhitneyU@ and @S.Tests.WilcoxonT@ * @sortBy@ is added to @S.Function@. * Mean and variance for gamma distribution are fixed. * Much faster cumulative probablity functions for Poisson and hypergeometric distributions. * Better density functions for gamma and Poisson distributions. * Student-T, Fisher-Snedecor F-distributions and Cauchy-Lorentz distrbution are added. * The function @S.Function.create@ is removed. Use @generateM@ from the @vector@ package instead. * Function to perform approximate comparion of doubles is added to @S.Function.Comparison@ * Regularized incomplete beta function and its inverse are added to @S.Function@.

## Versions

v0.13.3.0 :: 0/0.13.3.0 :: gentoo

- Modified
- License
- BSD
- Keywords
- ~amd64 ~x86
- USE flags
- doc hscolour profile test

v0.13.2.3 :: 0/0.13.2.3 :: gentoo

- Modified
- License
- BSD
- Keywords
- ~amd64 ~x86
- USE flags
- doc hscolour profile test

v0.11.0.3 :: 0/0.11.0.3 :: gentoo

- Modified
- License
- BSD
- Keywords
- ~amd64 ~x86
- USE flags
- doc hscolour profile test

v0.10.5.2 :: 0/0.10.5.2 :: gentoo

- Modified
- License
- BSD
- Keywords
- ~amd64 ~x86
- USE flags
- doc hscolour profile test

## USE flags

### General

- doc
- Add extra documentation (API, Javadoc, etc). It is recommended to enable per package instead of globally
- hscolour
- Include coloured haskell sources to generated documentation (dev-haskell/hscolour)
- profile
- Add support for software performance analysis (will likely vary from ebuild to ebuild)
- test
- Enable dependencies and/or preparations necessary to run tests (usually controlled by FEATURES=test but can be toggled independently)

## Dependencies

dev-haskell / aeson : Fast JSON parsing and encoding

dev-haskell / binary : Binary serialisation for Haskell values using lazy ByteStrings

dev-haskell / cabal : A framework for packaging Haskell software

dev-haskell / erf : The error function, erf, and related functions

dev-haskell / haddock : A documentation-generation tool for Haskell libraries

dev-haskell / hscolour : Colourise Haskell code

dev-haskell / hunit : A unit testing framework for Haskell

dev-haskell / ieee754 : Utilities for dealing with IEEE floating point numbers

dev-haskell / math-functions : Special functions and Chebyshev polynomials

dev-haskell / monad-par : A library for parallel programming based on a monad

dev-haskell / mwc-random : Fast, high quality pseudo random number generation

dev-haskell / primitive : Primitive memory-related operations

dev-haskell / quickcheck : Automatic testing of Haskell programs

dev-haskell / test-framework : Framework for running and organising tests, with HUnit and QuickCheck support

dev-haskell / test-framework-hunit : HUnit support for the test-framework package

dev-haskell / test-framework-quickcheck2 : QuickCheck2 support for the test-framework package

dev-haskell / vector : Efficient Arrays

dev-haskell / vector-algorithms : Efficient algorithms for vector arrays

dev-haskell / vector-binary-instances : Instances of Data.Binary and Data.Serialize for vector

## Runtime Dependencies

dev-haskell / aeson : Fast JSON parsing and encoding

dev-haskell / binary : Binary serialisation for Haskell values using lazy ByteStrings

dev-haskell / erf : The error function, erf, and related functions

dev-haskell / math-functions : Special functions and Chebyshev polynomials

dev-haskell / monad-par : A library for parallel programming based on a monad

dev-haskell / mwc-random : Fast, high quality pseudo random number generation

dev-haskell / primitive : Primitive memory-related operations

dev-haskell / vector : Efficient Arrays

dev-haskell / vector-algorithms : Efficient algorithms for vector arrays

dev-haskell / vector-binary-instances : Instances of Data.Binary and Data.Serialize for vector

## Depending packages

dev-haskell / criterion : Robust, reliable performance measurement and analysis

dev-haskell / mwc-random : Fast, high quality pseudo random number generation

## Change logs

- Robin H. Johnson · gentoo

Drop $Id$ per council decision in bug #611234.

Signed-off-by: Robin H. Johnson <robbat2@gentoo.org> - T. Malfatti · gentoo

media-libs/portaudio: Version bump - Sergei Trofimovich · gentoo

dev-haskell/statistics: bump up to 0.13.3.0

Package-Manager: portage-2.3.2 - Robin H. Johnson · gentoo

proj/gentoo: Initial commit

This commit represents a new era for Gentoo: Storing the gentoo-x86 tree in Git, as converted from CVS. This commit is the start of the NEW history. Any historical data is intended to be grafted onto this point. Creation process: 1. Take final CVS checkout snapshot 2. Remove ALL ChangeLog* files 3. Transform all Manifests to thin 4. Remove empty Manifests 5. Convert all stale $Header$/$Id$ CVS keywords to non-expanded Git $Id$ 5.1. Do not touch files with -kb/-ko keyword flags. Signed-off-by: Robin H. Johnson <robbat2@gentoo.org> X-Thanks: Alec Warner <antarus@gentoo.org> - did the GSoC 2006 migration tests X-Thanks: Robin H. Johnson <robbat2@gentoo.org> - infra guy, herding this project X-Thanks: Nguyen Thai Ngoc Duy <pclouds@gentoo.org> - Former Gentoo developer, wrote Git features for the migration X-Thanks: Brian Harring <ferringb@gentoo.org> - wrote much python to improve cvs2svn X-Thanks: Rich Freeman <rich0@gentoo.org> - validation scripts X-Thanks: Patrick Lauer <patrick@gentoo.org> - Gentoo dev, running new 2014 work in migration X-Thanks: Michał Górny <mgorny@gentoo.org> - scripts, QA, nagging X-Thanks: All of other Gentoo developers - many ideas and lots of paint on the bikeshed