./math/hs-statistics, Library of statistical types, data, and functions

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Branch: CURRENT, Version: 0.16.2.1nb2, Package name: hs-statistics-0.16.2.1nb2, Maintainer: pkgsrc-users

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.

Master sites:

Filesize: 105.369 KB

Version history: (Expand)

CVS history: (Expand)


   2024-05-09 03:32:57 by Masatake Daimon | Files touched by this commit (1137) 
Log message:
Recursive revbump after changing the default Haskell compiler
   2024-05-02 13:21:59 by Masatake Daimon | Files touched by this commit (1) 
Log message:
math/hs-statistics: Fix build with GHC 9.8

This breaks build with the currently default GHC 9.6. Please bear with me
until I switch the default compiler.
   2023-11-02 07:37:49 by Masatake Daimon | Files touched by this commit (1141) 
Log message:
Revbump all Haskell after updating lang/ghc96
   2023-11-02 03:47:40 by Masatake Daimon | Files touched by this commit (5) 
Log message:
math/hs-statistics: import hs-statistics-0.16.2.1

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.