name: learning-hmm version: 0.1.0.0 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. category: Algorithms, Machine Learning, Statistics author: Mitsuhiro Nakamura maintainer: Mitsuhiro Nakamura 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 source-repository head type: git location: https://github.com/mnacamura/learning-hmm.git library exposed-modules: Learning.HMM other-modules: Data.Random.Distribution.Categorical.Util , Data.Vector.Util , Data.Vector.Util.LinearAlgebra , Learning.HMM.Internal -- other-extensions: build-depends: base >=4.7 && <4.8 , logfloat , random-fu , vector hs-source-dirs: src default-language: Haskell2010 ghc-options: -Wall test-suite doctests main-is: doctests.hs type: exitcode-stdio-1.0 build-depends: base, doctest >= 0.9.11 hs-source-dirs: tests default-language: Haskell2010 ghc-options: -threaded -Wall