Name: HLearn-distributions Version: 0.2.2.1 Synopsis: Distributions for use with the HLearn library Description: This module is used to estimate statistical distributions from data. The focus is a clean interface inspired by algebra. Category: Data Mining, Machine Learning, Statistics License: GPL --License-file: LICENSE Author: Mike izbicki Maintainer: mike@izbicki.me Build-Type: Simple Cabal-Version: >=1.8 homepage: http://github.com/mikeizbicki/HLearn/ bug-reports: http://github.com/mikeizbicki/HLearn/issues Executable HLearn-Distributions-Criterion Main-is: src/examples/Criterion.hs Build-Depends: HLearn-algebra >= 0.0.1, ConstraintKinds >= 0.0.1, HLearn-distributions >= 0.0.1, base >= 3 && < 5, criterion >= 0.6.1.1, vector, -- logfloat , statistics ghc-options: -threaded -rtsopts -O2 -funbox-strict-fields -- -prof -- -fllvm Executable HLearn-Distributions-SpaceTests Main-is: src/examples/SpaceTests.hs Build-Depends: HLearn-algebra >= 0.0.1, --HLearn-algebra , ConstraintKinds, HLearn-distributions , base >= 3 && < 5, criterion >= 0.6.1.1, vector, logfloat , statistics ghc-options: -threaded -rtsopts -O2 -funbox-strict-fields --enable-executable-profiling -- -prof -- -fllvm Library Build-Depends: HLearn-algebra >= 0.1.2, ConstraintKinds >= 0.0.1, base >= 3 && < 5, deepseq >= 1.3.0.1, list-extras >= 0.4.1, containers >= 0.5, statistics >= 0.10.2, QuickCheck >= 2.5.1, vector >= 0.9, vector-th-unbox >= 0.2, -- are these really necessary? process >= 1.1.0.2, MonadRandom >= 0.1.6, math-functions >= 0.1.1, normaldistribution >= 1.1.0 hs-source-dirs: src ghc-options: -rtsopts -- -auto-all -- -caf-all -funbox-strict-fields -O2 -- -fllvm Exposed-modules: HLearn.Gnuplot.Distributions HLearn.Models.Distributions HLearn.Models.Distributions.Common HLearn.Models.Distributions.Categorical HLearn.Models.Distributions.KernelDensityEstimator HLearn.Models.Distributions.KernelDensityEstimator.Kernels HLearn.Models.Distributions.Moments HLearn.Models.Distributions.Multivariate --HLearn.Models.Distributions.Normal HLearn.Models.Distributions.Gaussian --HLearn.Models.Distributions.GaussianOld --HLearn.Models.Distributions.GaussianOld2 --HLearn.Models.Distributions.Poisson