Rcpp Hidden Markov Model

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.


News

Release v1.2 (August 2017)

-The class of HMM with observations being modelled by a Gaussian Mixture Model (GHMM) was updated to have also a multivariated version. -The emission matrix of the GHMM model was divided into two parameters: Mu and Sigma. Mu is now a 2D matrix with number of rows equal to the observation vector dimensionality and the number of columns equal to the number of hidden states. Sigma is now a 3D matrix with number of rows and columns equal to the the observation vector dimensionality and the number of slices equal to the number of hidden states.


Release v1.1 (May 2017) -Since there are different classes of HMMs and each of them with the same algorithms, a verification step was added to avoid memory leaks and variable compatibility.

Reference manual

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install.packages("RcppHMM")

1.2.2 by Roberto A. Cardenas-Ovando, a year ago


Browse source code at https://github.com/cran/RcppHMM


Authors: Roberto A. Cardenas-Ovando , Julieta Noguez and Claudia Rangel-Escareno


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Rcpp

Linking to Rcpp, RcppArmadillo

System requirements: C++11


See at CRAN