ST +Control.Monad +package
Provides an API for inserting heterogeneous data in a collection keyed by StableNames and for later retrieving it.
Fundamental * -> * types, operators, and covariant instances.
Contravariant instances for the fundamental * -> * types and operators.
A haskell memcached client. See http://memcached.org for more information.
This implements the new binary protocol, so it only works with memcached version 1.3 and newer.
Space simulation game.
This package provides a Template Haskell function which transforms a normal record declaration into one which supports many useful operations when used as the state in a State monad.
A MonadST type class, instances, and some helpful monad functions.
A collection of type-classes generalizing the read/write/modify operations for stateful variables provided by things like IORef, TVar, &c. Note that The interface has changed a bit from the 0.2.* version. "*Ref" functions are now called "*Reference" and new "*Ref" function exist with simpler signatures. The new Ref existential type provides a convenient monad-indexed reference type, and the HasRef class indicates monads for which there is a default reference type for every referent.
Simple State-like monad transformer where states can be saved to and restored from an internal stack.
The ST monad and STRefs in a portable form. This package implements state threads as wrapper around IO and IORefs. Your compiler must support rank-2-types, IORefs, unsafePerformIO and unsafeInterleaveIO. The package can be used as drop-in replacement for the st package.
This package contains state variables, which are references in the IO monad, like IORefs or parts of the OpenGL state.
Pure immutable hash whose lookup is O(1)
You need to add static resources to your web page. For production you want to decrease number of files. For development you need them separated. Support for distinct sets of JS and CSS files for different views.
High-level statistical methods.
* Confusion matrix
* Confusion matrix dependent statistics (sensitivity, specificity, F-measure, mcc)
* EM algorithm for two-component Gaussian mixture.
* GMM (Gaussian Mixture Models) with >=1 Gaussians fitted to the data.
Note that some methods are for testing only (two-component Gaussian mixture EM).
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 autocorrelation analysis.
* Random variate generation under several different distributions.
* Common statistical tests for significant differences between samples.
Changes in 0.10.1.0
* Kolmogorov-Smirnov nonparametric test added.
* Pearson's 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-functions package. They are still available but marked as deprecated.
Changed in 0.10.0.1
* dct and idct now 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.
* Forward and inverse discrete Fourier and cosine transforms are 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 Bool.
* Performance improvements for Mann-Whitney U and Wilcoxon tests.
* Module S.Tests.NonParamtric is split into S.Tests.MannWhitneyU and S.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.
* 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.
Functions for working with Dirichlet densities and mixtures on vectors. The focus of this package is on deriving these distributions from observed data.
This package should be treated as experimental code, it has not been battle-tested as much as it would be nice to be.
Note that although this package is BSD3-licensed, it uses the nonlinear-optimization package which is GPLed. It should be straightforward to use another library in its stead, though.
Provides a function to perform a linear regression between 2 samples, see the documentation of the linearRegression function. This library is based on the statistics package.
* 2.*: added the r-squared version and improved the performances
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