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Fits hidden Markov models with discrete nonparametric 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.
Package details 


Author  Rolf Turner 
Maintainer  Rolf Turner <r.turner@auckland.ac.nz> 
License  GPL (>= 2) 
Version  3.09 
Package repository  View on CRAN 
Installation 
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