HLearn-distributions-1.0.0.1: HLearn-distributions.cabal

Name:                HLearn-distributions
Version:             1.0.0.1
Synopsis:            Distributions for use with the HLearn library
Description:         This module is used to estimate statistical distributions from data.  It is based on the algebraic properties of the "HomTrainer" type class from the HLearn-algebra package.
Category:            Data Mining, Machine Learning, Statistics
License:             BSD3
--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

Library
    Build-Depends:      
        HLearn-algebra          >= 1.0.0.1,
        ConstraintKinds         >= 0.0.1,
        base                    >= 3 && < 5,
        
        template-haskell,
        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,
        graphviz                >= 2999.16,
        hmatrix                 >= 0.14,
        
        -- are these really necessary?
        array                   >= 0.4.0,
        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.Models.Distributions
        HLearn.Models.Distributions.Common
        HLearn.Models.Distributions.Visualization.Gnuplot
        HLearn.Models.Distributions.Visualization.Graphviz
        HLearn.Models.Distributions.Univariate.Binomial
        HLearn.Models.Distributions.Univariate.Categorical
        HLearn.Models.Distributions.Univariate.Exponential
        HLearn.Models.Distributions.Univariate.Geometric
        HLearn.Models.Distributions.Univariate.LogNormal
        HLearn.Models.Distributions.Univariate.Normal
        HLearn.Models.Distributions.Univariate.Poisson
        --HLearn.Models.Distributions.Univariate.Uniform
        HLearn.Models.Distributions.Univariate.Internal.MissingData
        HLearn.Models.Distributions.Univariate.Internal.Moments
        HLearn.Models.Distributions.Multivariate.Interface
        HLearn.Models.Distributions.Multivariate.MultiNormal
        HLearn.Models.Distributions.Multivariate.Internal.CatContainer
        HLearn.Models.Distributions.Multivariate.Internal.Container
        HLearn.Models.Distributions.Multivariate.Internal.Ignore
        HLearn.Models.Distributions.Multivariate.Internal.Marginalization
        HLearn.Models.Distributions.Multivariate.Internal.TypeLens
        HLearn.Models.Distributions.Multivariate.Internal.Unital