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
47 modules- Statistics.Autocorrelation2Functions for computing autocovariance and autocorrelation of a
- Statistics.ConfidenceInt4Calculation of confidence intervals
- Statistics.Correlation6
- Statistics.Correlation.Kendall1Fast O(NlogN) implementation of
- Statistics.Distribution15Type classes for probability distributions
- Statistics.Distribution.Beta7
- Statistics.Distribution.Binomial5The binomial distribution. This is the discrete probability
- Statistics.Distribution.CauchyLorentz6The Cauchy-Lorentz distribution. It's also known as Lorentz
- Statistics.Distribution.ChiSquared4The chi-squared distribution. This is a continuous probability
- Statistics.Distribution.DiscreteUniform5The discrete uniform distribution. There are two parametrizations of
- Statistics.Distribution.Exponential4The exponential distribution. This is the continuous probability
- Statistics.Distribution.FDistribution7Fisher F distribution
- Statistics.Distribution.Gamma7The gamma distribution. This is a continuous probability
- Statistics.Distribution.Geometric8The Geometric distribution. There are two variants of
- Statistics.Distribution.Hypergeometric6The Hypergeometric distribution. This is the discrete probability
- Statistics.Distribution.Laplace5The Laplace distribution. This is the continuous probability
- Statistics.Distribution.Lognormal5The log normal distribution. This is a continuous probability
- Statistics.Distribution.NegativeBinomial5The negative binomial distribution. This is the discrete probability
- Statistics.Distribution.Normal5The normal distribution. This is a continuous probability
- Statistics.Distribution.Poisson4The Poisson distribution. This is the discrete probability
- Statistics.Distribution.StudentT5Student-T distribution
- Statistics.Distribution.Transform3Transformations over distributions
- Statistics.Distribution.Uniform5Variate distributed uniformly in the interval.
- Statistics.Distribution.Weibull5The Weibull distribution. This is a continuous probability
- Statistics.Function13Useful functions.
- Statistics.Quantile16Functions for approximating quantiles, i.e. points taken at regular
- Statistics.Regression4Functions for regression analysis.
- Statistics.Resampling13Resampling statistics.
- Statistics.Resampling.Bootstrap2The bootstrap method for statistical inference.
- Statistics.Sample28Commonly used sample statistics, also known as descriptive
- Statistics.Sample.Histogram3Functions for computing histograms of sample data.
- Statistics.Sample.KernelDensity2Kernel density estimation. This module provides a fast, robust,
- Statistics.Sample.KernelDensity.Simple13Kernel density estimation code, providing non-parametric ways to
- Statistics.Sample.Normalize1Functions for normalizing samples.
- Statistics.Sample.Powers12Very fast statistics over simple powers of a sample. These can all
- Statistics.Test.Bartlett4Bartlett's test is used to check that multiple groups of observations
- Statistics.Test.ChiSquared2Pearson's chi squared test.
- Statistics.Test.KolmogorovSmirnov7Kolmogov-Smirnov tests are non-parametric tests for assessing
- Statistics.Test.KruskalWallis3
- Statistics.Test.Levene2Levene's test used to check whether samples have equal variance. Null
- Statistics.Test.MannWhitneyU8Mann-Whitney U test (also know as Mann-Whitney-Wilcoxon and
- Statistics.Test.StudentT3Student's T-test is for assessing whether two samples have
- Statistics.Test.Types5
- Statistics.Test.WilcoxonT5The Wilcoxon matched-pairs signed-rank test is non-parametric test
- Statistics.Transform7Fourier-related transformations of mathematical functions. These functions are written for simplicity and correctness, not
- Statistics.Types33Data types common used in statistics
Internal modules · 1
- Statistics.Sample.Internal3Internal 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-2.3.2.0in 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