Hidden Markov Models with Discrete Non-Parametric Observation Distributions

Fits hidden Markov models with discrete non-parametric observation distributions to data sets. The observations may be univariate or bivariate. Simulates data from such models. Finds most probable underlying hidden states, the most probable sequences of such states, and the log likelihood of a collection of observations given the parameters of the model. Auxiliary predictors are accommodated in the univariate setting.


Reference manual

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2.1-12 by Rolf Turner, a day ago

Browse source code at https://github.com/cran/hmm.discnp

Authors: Rolf Turner

Documentation:   PDF Manual  

GPL (>= 2) license

Imports nnet

See at CRAN