Hidden Markov Models with Discrete Non-Parametric Observation
Distributions
Description
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.