Package0.16.5.0MathStatistics
statistics
A library of statistical types, data, and functions
- Version0.16.5.0
- CategoryMath, Statistics
- LicenceBSD-2-Clause
- AuthorBryan O'Sullivan <bos@serpentine.com>, Alexey Khudaykov <alexey.skladnoy@gmail.com>
- MaintainerAlexey Khudaykov <alexey.skladnoy@gmail.com>
- Homepagegithub.com/haskell/statistics
- Pinned byhackage statistics 0.16.5.0
- Sourcehackage.haskell.org/package/statistics-0.16.5.0
Modules
- Statistics.AutocorrelationFunctions for computing autocovariance and autocorrelation of a
- Statistics.ConfidenceIntCalculation of confidence intervals
- Statistics.Correlation
- Statistics.Correlation.KendallFast O(NlogN) implementation of
- Statistics.DistributionType classes for probability distributions
- Statistics.Distribution.Beta
- Statistics.Distribution.BinomialThe binomial distribution. This is the discrete probability
- Statistics.Distribution.CauchyLorentzThe Cauchy-Lorentz distribution. It's also known as Lorentz
- Statistics.Distribution.ChiSquaredThe chi-squared distribution. This is a continuous probability
- Statistics.Distribution.DiscreteUniformThe discrete uniform distribution. There are two parametrizations of
- Statistics.Distribution.ExponentialThe exponential distribution. This is the continuous probability
- Statistics.Distribution.FDistributionFisher F distribution
- Statistics.Distribution.GammaThe gamma distribution. This is a continuous probability
- Statistics.Distribution.GeometricThe Geometric distribution. There are two variants of
- Statistics.Distribution.HypergeometricThe Hypergeometric distribution. This is the discrete probability
- Statistics.Distribution.LaplaceThe Laplace distribution. This is the continuous probability
- Statistics.Distribution.LognormalThe log normal distribution. This is a continuous probability
- Statistics.Distribution.NegativeBinomialThe negative binomial distribution. This is the discrete probability
- Statistics.Distribution.NormalThe normal distribution. This is a continuous probability
- Statistics.Distribution.PoissonThe Poisson distribution. This is the discrete probability
- Statistics.Distribution.StudentTStudent-T distribution
- Statistics.Distribution.TransformTransformations over distributions
- Statistics.Distribution.UniformVariate distributed uniformly in the interval.
- Statistics.Distribution.WeibullThe Weibull distribution. This is a continuous probability
- Statistics.FunctionUseful functions.
- Statistics.QuantileFunctions for approximating quantiles, i.e. points taken at regular
- Statistics.RegressionFunctions for regression analysis.
- Statistics.ResamplingResampling statistics.
- Statistics.Resampling.BootstrapThe bootstrap method for statistical inference.
- Statistics.SampleCommonly used sample statistics, also known as descriptive
- Statistics.Sample.HistogramFunctions for computing histograms of sample data.
- Statistics.Sample.KernelDensityKernel density estimation. This module provides a fast, robust,
- Statistics.Sample.KernelDensity.SimpleKernel density estimation code, providing non-parametric ways to
- Statistics.Sample.NormalizeFunctions for normalizing samples.
- Statistics.Sample.PowersVery fast statistics over simple powers of a sample. These can all
- Statistics.Test.BartlettBartlett's test is used to check that multiple groups of observations
- Statistics.Test.ChiSquaredPearson's chi squared test.
- Statistics.Test.KolmogorovSmirnovKolmogov-Smirnov tests are non-parametric tests for assessing
- Statistics.Test.KruskalWallis
- Statistics.Test.LeveneLevene's test used to check whether samples have equal variance. Null
- Statistics.Test.MannWhitneyUMann-Whitney U test (also know as Mann-Whitney-Wilcoxon and
- Statistics.Test.StudentTStudent's T-test is for assessing whether two samples have
- Statistics.Test.Types
- Statistics.Test.WilcoxonTThe Wilcoxon matched-pairs signed-rank test is non-parametric test
- Statistics.TransformFourier-related transformations of mathematical functions. These functions are written for simplicity and correctness, not
- Statistics.TypesData types common used in statistics
Internal modules · 1
- Statistics.Sample.InternalInternal functions for computing over samples.
Description
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.
Depends on
16 packages- aeson-2.2.5.1in this set
- async-2.2.6in this set
- base-4.23.0.0with GHC
- binary-0.8.9.3with GHC
- data-default-class-0.2.0.0in this set
- deepseq-1.5.2.0with GHC
- dense-linear-algebra-0.1.0.0in this set
- math-functions-0.3.4.4in this set
- mwc-random-0.15.3.0in this set
- parallel-3.3.0.0in this set
- primitive-0.9.1.0in this set
- random-1.3.1in this set
- vector-0.13.2.0in this set
- vector-algorithms-0.9.1.0in this set
- vector-binary-instances-0.2.5.2in this set
- vector-th-unbox-0.2.2in this set