Module documentation for 0.13.2.3
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.
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.
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.)
This library is written and maintained by Bryan O’Sullivan, firstname.lastname@example.org.
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 p-value. Also API for statistical tests is changed.
Statistis.Typesnow contains new data types for estimates, upper/lower bounds, confidence level, and p-value.
CLfor representing confidence level
PValuefor representing p-values
Estimatedata type moved here from
Statistis.Resampling.Bootstrapand now parametrized by type of error.
NormalError— represents normal error.
ConfInt— generic confidence interval
LowerLimitfor upper/lower limits.
New API for statistical tests. Instead of simply return significant/not significant it returns p-value, test statistics and distribution of test statistics if it’s available. Tests also return
Nothinginstead of throwing error if sample size is not sufficient. Fixes #25.
Statistics.Tests.Types.TestTypedata type dropped
New smart constructors for distributions are added. They return
Nothingif 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 so
Binaryinstances changed normal and F-distributions.
Encoding to JSON changed for Normal, F-distribution, and χ² distributions. However data created using older statistics will be successfully decoded.
Statistics.Resample.Bootstrap uses new data types for central estimates.
Function for calculation of confidence intervals for Poisson and binomial distribution added in
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 Wilcoxon-T, Mann-Whitney-U, and Student-T tests.
API for bootstrap changed. New data types added.
Bug fixes for #74, #81, #83, #92, #94
complCumulativeadded 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
Function for calculation covariance added in
Statistics.Function.pairadded. It zips vector and check that lengths are equal.
New functions added to
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
Changes in 0.13.1.0
- All types now support JSON encoding and decoding.
Changes in 0.12.0.0
Statistics.Mathmodule has been removed, after being deprecated for several years. Use the math-functions package instead.
Statistics.Test.NonParametricmodule 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 Kruskal-Wallis 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 continous 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 Wilcoxon-T test is fixed.
Bug in ‘ContGen’ instance for normal distribution is fixed.
Changes in 0.10.1.0
Kolmogorov-Smirnov 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
math-functionspackage. They are still available but marked as deprecated.
Changes in 0.10.0.1
idctnow have type
Vector 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 multi-modal 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 one-tailed 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 chi-square and gamma distribution are fixed.
Integer overlow in mannWhitneyUCriticalValue is fixed. It produced incorrect critical values for moderately large samples. Something around 20 for 32-bit machines and 40 for 64-bit ones.
A bug in mannWhitneyUSignificant is fixed. If either sample was larger than 20, it produced a completely incorrect answer.
One- and two-tailed tests in S.Tests.NonParametric are selected with sum types instead of Bool.
Test results returned as enumeration instead of
Performance improvements for Mann-Whitney U and Wilcoxon tests.
S.Tests.NonParamtricis split into
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.
Student-T, Fisher-Snedecor F-distributions and Cauchy-Lorentz 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