name:                learning-hmm
version:             0.3.0.0
stability:           experimental

synopsis:            Yet another library for hidden Markov models
description:         This library provides functions for the maximum likelihood
                     estimation of discrete hidden Markov models. At present,
                     only Baum-Welch and Viterbi algorithms are implemented for
                     the plain HMM and the input-output HMM.
category:            Algorithms, Machine Learning, Statistics

author:              Mitsuhiro Nakamura
maintainer:          Mitsuhiro Nakamura <m.nacamura@gmail.com>
copyright:           Copyright (c) 2014 Mitsuhiro Nakamura
license:             MIT
license-file:        LICENSE
homepage:            https://github.com/mnacamura/learning-hmm

cabal-version:       >=1.10
build-type:          Simple
extra-source-files:  CHANGES.md

source-repository head
  type:              git
  location:          https://github.com/mnacamura/learning-hmm.git

library
  exposed-modules:   Learning.HMM
                   , Learning.IOHMM
  other-modules:     Data.Random.Distribution.Categorical.Util
                   , Data.Random.Distribution.Simplex
                   , Data.Vector.Generic.Util
                   , Learning.HMM.Internal
                   , Learning.IOHMM.Internal
  -- other-extensions:  
  build-depends:     base >=4.7 && <4.8
                   , containers
                   , deepseq
                   , hmatrix < 0.16.1
                   , random-fu
                   , random-source
                   , vector
  hs-source-dirs:    src
  default-language:  Haskell2010
  ghc-options:       -Wall