Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.
|Roberto A. Cardenas-Ovando, Julieta Noguez and Claudia Rangel-Escareno
|Roberto A. Cardenas-Ovando <firstname.lastname@example.org>
|GPL (>= 2)
|View on CRAN
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