Rcpp Hidden Markov Model
Changes Made to Package RcppHMM
Observed sequence evaluation given a model
Forward-backward algortihm for hidden state decoding
Generate observations given a model
Random Initialization for a Hidden Markov Model with emissions modeled...
Random Initialization for a Hidden Markov Model with emissions modeled...
Random Initialization for a Hidden Markov Model with emissions modeled...
Expectation-Maximization algorithm to estimate the model parameters
Evaluation of multiple observed sequences given a model
Overview of Package RcppHMM
Set the names of the model
Set the model parameters
Model parameter verification
Viterbi algorithm for hidden state decoding
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.