statistics
A library of statistical types, data, and functions https://github.com/bos/statistics
Version on this page:  0.14.0.2 
LTS Haskell 12.19:  0.14.0.2 
Stackage Nightly 20181118:  0.15.0.0 
Latest on Hackage:  0.15.0.0 
Module documentation for 0.14.0.2
There are no documented modules for this package.
Statistics: efficient, general purpose statistics
This package provides the Statistics module, a Haskell library for working with statistical data in a space and timeefficient 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 the github 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.)
Authors
This library is written and maintained by Bryan O’Sullivan, bos@serpentine.com.
Changes
Changes in 0.15.0.0

Modules
Statistics.Matrix.*
are split into new packagedenselinearalgebra
and exponent field is removed fromMatrix
data type. 
Module
Statistics.Normalize
which contains functions for normalization of samples 
Module
Statistics.Quantile
reworked:ContParam
givenDefault
instancequantile
should be used instead ofcontinuousBy
median
andmad
are addedquantiles
andquantilesVec
functions for computation of set of quantiles added.

Modules
Statistics.Function.Comparison
andStatistics.Math.RootFinding
are removed. Corresponding functionality could be found inmathfunctions
package. 
Fix vector index out of bounds in
bootstrapBCA
andbootstrapRegress
(see issue #149)
Changes in 0.14.0.2
 Compatibility fixes with older GHC
Changes in 0.14.0.1
 Restored compatibility with GHC 7.4 & 7.6
Changes in 0.14.0.0
Breaking update. It seriously changes parts of API. It adds new data types for dealing with with estimates, confidence intervals, confidence levels and pvalue. Also API for statistical tests is changed.

Module
Statistis.Types
now contains new data types for estimates, upper/lower bounds, confidence level, and pvalue.CL
for representing confidence levelPValue
for representing pvaluesEstimate
data type moved here fromStatistis.Resampling.Bootstrap
and now parametrized by type of error.NormalError
— represents normal error.ConfInt
— generic confidence intervalUpperLimit
,LowerLimit
for upper/lower limits.

New API for statistical tests. Instead of simply return significant/not significant it returns pvalue, test statistics and distribution of test statistics if it’s available. Tests also return
Nothing
instead of throwing error if sample size is not sufficient. Fixes #25. 
Statistics.Tests.Types.TestType
data type dropped 
New smart constructors for distributions are added. They return
Nothing
if parameters are outside of allowed range. 
Serialization instances (
Show/Read, Binary, ToJSON/FromJSON
) for distributions no longer allows to create data types with invalid parameters. They will fail to parse. Cached values are not serialized either soBinary
instances changed normal and Fdistributions.Encoding to JSON changed for Normal, Fdistribution, and χ² distributions. However data created using older statistics will be successfully decoded.
Fixes #59.

Statistics.Resample.Bootstrap uses new data types for central estimates.

Function for calculation of confidence intervals for Poisson and binomial distribution added in
Statistics.ConfidenceInt

Tests of position now allow to ask whether first sample on average larger than second, second larger than first or whether they differ significantly. Affects WilcoxonT, MannWhitneyU, and StudentT tests.

API for bootstrap changed. New data types added.

Bug fixes for #74, #81, #83, #92, #94

complCumulative
added for many distributions.
Changes in 0.13.3.0

Kernel density estimation and FFT use generic versions now.

Code for calculation of Spearman and Pearson correlation added. Modules
Statistics.Correlation.Spearman
andStatistics.Correlation.Pearson
. 
Function for calculation covariance added in
Statistics.Sample
. 
Statistics.Function.pair
added. It zips vector and check that lengths are equal. 
New functions added to
Statistics.Matrix

Laplace distribution added.
Changes in 0.13.2.3
 Vector dependency restored to >=0.10
Changes in 0.13.2.2
 Vector dependency lowered to >=0.9
Changes in 0.13.2.1
 Vector dependency bumped to >=0.10
Changes in 0.13.2.0
 Support for regression bootstrap added
Changes in 0.13.1.1
 Fix for out of bound access in bootstrap (see
bos/criterion#52
)
Changes in 0.13.1.0
 All types now support JSON encoding and decoding.
Changes in 0.12.0.0

The
Statistics.Math
module has been removed, after being deprecated for several years. Use the mathfunctions package instead. 
The
Statistics.Test.NonParametric
module has been removed, after being deprecated for several years. 
Added support for Kendall’s tau.

Added support for OLS regression.

Added basic 2D matrix support.

Added the KruskalWallis test.
Changes in 0.11.0.3

Fixed a subtle bug in calculation of the jackknifed unbiased variance.

The test suite now requires QuickCheck 2.7.

We now calculate quantiles for normal distribution in a more numerically stable way (bug #64).
Changes in 0.10.6.0

The Estimator type has become an algebraic data type. This allows the jackknife function to potentially use more efficient jackknife implementations.

jackknifeMean, jackknifeStdDev, jackknifeVariance, jackknifeVarianceUnb: new functions. These have O(n) cost instead of the O(n^2) cost of the standard jackknife.

The mean function has been renamed to welfordMean; a new implementation of mean has better numerical accuracy in almost all cases.
Changes in 0.10.5.2
 histogram correctly chooses range when all elements in the sample are same (bug #57)
Changes in 0.10.5.1
 Bug fix for S.Distributions.Normal.standard introduced in 0.10.5.0 (Bug #56)
Changes in 0.10.5.0

Enthropy type class for distributions is added.

Probability and probability density of distribution is given in log domain too.
Changes in 0.10.4.0

Support for versions of GHC older than 7.2 is discontinued.

All datatypes now support ‘Data.Binary’ and ‘GHC.Generics’.
Changes in 0.10.3.0
 Bug fixes
Changes in 0.10.2.0

Bugs in DCT and IDCT are fixed.

Accesors for uniform distribution are added.

ContGen instances for all continuous distribtuions are added.

Beta distribution is added.

Constructor for improper gamma distribtuion is added.

Binomial distribution allows zero trials.

Poisson distribution now accept zero parameter.

Integer overflow in caculation of WilcoxonT test is fixed.

Bug in ‘ContGen’ instance for normal distribution is fixed.
Changes in 0.10.1.0

KolmogorovSmirnov nonparametric test added.

Pearson chi squared test added.

Type class for generating random variates for given distribution is added.

Modules ‘Statistics.Math’ and ‘Statistics.Constants’ are moved to the
mathfunctions
package. They are still available but marked as deprecated.
Changes in 0.10.0.1
dct
andidct
now have typeVector Double > Vector Double
Changes in 0.10.0.0

The type classes Mean and Variance are split in two. This is required for distributions which do not have finite variance or mean.

The S.Sample.KernelDensity module has been renamed, and completely rewritten to be much more robust. The older module oversmoothed multimodal data. (The older module is still available under the name S.Sample.KernelDensity.Simple).

Histogram computation is added, in S.Sample.Histogram.

Discrete Fourie transform is added, in S.Transform

Root finding is added, in S.Math.RootFinding.

The complCumulative function is added to the Distribution class in order to accurately assess probalities P(X>x) which are used in onetailed tests.

A stdDev function is added to the Variance class for distributions.

The constructor S.Distribution.normalDistr now takes standard deviation instead of variance as its parameter.

A bug in S.Quantile.weightedAvg is fixed. It produced a wrong answer if a sample contained only one element.

Bugs in quantile estimations for chisquare and gamma distribution are fixed.

Integer overlow in mannWhitneyUCriticalValue is fixed. It produced incorrect critical values for moderately large samples. Something around 20 for 32bit machines and 40 for 64bit ones.

A bug in mannWhitneyUSignificant is fixed. If either sample was larger than 20, it produced a completely incorrect answer.

One and twotailed tests in S.Tests.NonParametric are selected with sum types instead of Bool.

Test results returned as enumeration instead of
Bool
. 
Performance improvements for MannWhitney U and Wilcoxon tests.

Module
S.Tests.NonParamtric
is split intoS.Tests.MannWhitneyU
andS.Tests.WilcoxonT

sortBy is added to S.Function.

Mean and variance for gamma distribution are fixed.

Much faster cumulative probablity functions for Poisson and hypergeometric distributions.

Better density functions for gamma and Poisson distributions.

StudentT, FisherSnedecor Fdistributions and CauchyLorentz distrbution are added.

The function S.Function.create is removed. Use generateM from the vector package instead.

Function to perform approximate comparion of doubles is added to S.Function.Comparison

Regularized incomplete beta function and its inverse are added to S.Function