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math/hs-statistics,
Library of statistical types, data, and functions
Branch: CURRENT,
Version: 0.16.2.1nb2,
Package name: hs-statistics-0.16.2.1nb2,
Maintainer: pkgsrc-usersThis 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)
- (2024-05-09) Updated to version: hs-statistics-0.16.2.1nb2
- (2023-11-02) Updated to version: hs-statistics-0.16.2.1nb1
- (2023-11-02) Package added to pkgsrc.se, version hs-statistics-0.16.2.1 (created)
CVS history: (Expand)
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.
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