hmm: A hidden markov model library
[ algorithms, bsd3, data-mining, library, machine-learning ]
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Data.HMM is a library for using Hidden Markov Models with Haskell. Commonly used algoriths (i.e. the forward and backwards algorithms, Viterbi, and Baum-Welch) are implemented. The best way to learn to use it is to visit the tutorial at http://izbicki.me/blog/using-hmms-in-haskell-for-bioinformatics. The tutorial also includes performance benchmarks that you should be aware of.
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- hmm-0.2.1.1.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)
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| Versions [RSS] | 0.1, 0.1.1, 0.2.1, 0.2.1.1 |
|---|---|
| Dependencies | array, base (>=4 && <5), data-memocombinators, list-extras, logfloat [details] |
| License | BSD-3-Clause |
| Author | Mike Izbicki |
| Maintainer | mike@izbicki.me |
| Category | Algorithms, Data mining, Machine learning |
| Home page | https://github.com/mikeizbicki/hmm |
| Uploaded | by MikeIzbicki at 2012-03-26T19:37:56Z |
| Distributions | |
| Reverse Dependencies | 1 direct, 0 indirect [details] |
| Downloads | 3704 total (9 in the last 30 days) |
| Rating | (no votes yet) [estimated by Bayesian average] |
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| Status | Docs uploaded by user Build status unknown [no reports yet] |