statistics (original) (raw)
statistics: A library of statistical types, data, and functions
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.
Modules
[Index] [Quick Jump]
- Statistics
- Statistics.Autocorrelation
- Statistics.ConfidenceInt
- Statistics.Correlation
* Statistics.Correlation.Kendall - Statistics.Distribution
* Statistics.Distribution.Beta
* Statistics.Distribution.Binomial
* Statistics.Distribution.CauchyLorentz
* Statistics.Distribution.ChiSquared
* Statistics.Distribution.DiscreteUniform
* Statistics.Distribution.Exponential
* Statistics.Distribution.FDistribution
* Statistics.Distribution.Gamma
* Statistics.Distribution.Geometric
* Statistics.Distribution.Hypergeometric
* Statistics.Distribution.Laplace
* Statistics.Distribution.Lognormal
* Statistics.Distribution.NegativeBinomial
* Statistics.Distribution.Normal
* Statistics.Distribution.Poisson
* Statistics.Distribution.StudentT
* Statistics.Distribution.Transform
* Statistics.Distribution.Uniform
* Statistics.Distribution.Weibull - Statistics.Function
- Statistics.Quantile
- Statistics.Regression
- Statistics.Resampling
* Statistics.Resampling.Bootstrap - Statistics.Sample
* Statistics.Sample.Histogram
* Statistics.Sample.Internal
* Statistics.Sample.KernelDensity
* Statistics.Sample.KernelDensity.Simple
* Statistics.Sample.Normalize
* Statistics.Sample.Powers - Test
* Statistics.Test.ChiSquared
* Statistics.Test.KolmogorovSmirnov
* Statistics.Test.KruskalWallis
* Statistics.Test.MannWhitneyU
* Statistics.Test.StudentT
* Statistics.Test.Types
* Statistics.Test.WilcoxonT - Statistics.Transform
- Statistics.Types
Flags
Manual Flags
Name | Description | Default |
---|---|---|
benchpapi | Enable building of benchmarks which use instruction counters. It requires libpapi and only works on Linux so it's protected by flag | Disabled |
Use -f to enable a flag, or -f - to disable that flag. More info
Downloads
- statistics-0.16.3.0.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)
Maintainer's Corner
For package maintainers and hackage trustees
Candidates
- No Candidates
Readme for statistics-0.16.3.0
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 thegithub issue tracker.
Master git mirror:
git clone git://github.com/bos/statistics.git
There's also a Mercurial mirror:
hg clone https://bitbucket.org/bos/statistics
(You can create and contribute changes using either Mercurial or git.)
This library is written and maintained by Bryan O'Sullivan,bos@serpentine.com.