hmm.discnp

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.

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Description file content

Package
hmm.discnp
Version
2.1-12
Date
2019-12-05
Title
Hidden Markov Models with Discrete Non-Parametric Observation Distributions
Author
Rolf Turner
Maintainer
Rolf Turner
Depends
R (>= 2.10)
Imports
nnet (>= 7.3.12)
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.
LazyData
true
ByteCompile
true
License
GPL (>= 2)
NeedsCompilation
yes
Packaged
2019-12-05 11:22:05 UTC; rolf
Repository
CRAN
Date/Publication
2019-12-05 12:30:02 UTC

install.packages('hmm.discnp')

2.1-12

4 days ago

Rolf Turner

GPL (>= 2)

Depends on

R (>= 2.10)

Imports

nnet (>= 7.3.12)

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