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.

Version:	3.0-9
Depends:	R (≥ 2.10)
Imports:	nnet (≥ 7.3.12)
Published:	2022-09-26
DOI:	10.32614/CRAN.package.hmm.discnp
Author:	Rolf Turner
Maintainer:	Rolf Turner <r.turner at auckland.ac.nz>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Materials:	ChangeLog
CRAN checks:	hmm.discnp results

Documentation:

Reference manual:

hmm.discnp.pdf

Downloads:

Package source:	hmm.discnp_3.0-9.tar.gz
Windows binaries:	r-devel: hmm.discnp_3.0-9.zip, r-release: hmm.discnp_3.0-9.zip, r-oldrel: hmm.discnp_3.0-9.zip
macOS binaries:	r-release (arm64): hmm.discnp_3.0-9.tgz, r-oldrel (arm64): hmm.discnp_3.0-9.tgz, r-release (x86_64): hmm.discnp_3.0-9.tgz, r-oldrel (x86_64): hmm.discnp_3.0-9.tgz
Old sources:	hmm.discnp archive

Reverse dependencies:

Reverse suggests:

dbd

Linking:

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