hmmhdd: Hidden Markov Models for High Dimensional Data

Some algorithms for the study of Hidden Markov Models for two different types of data. For the study of univariate and multivariate data in a finite framework, we provide some methods based on the definition of a Gaussian copula function to define the dependence between data (for further details, see Martino A., Guatteri, G. and Paganoni A. M. (2018) <https://mox.polimi.it/publication-results/?id=776&tipo=add_qmox>). For the study of functional data, we define an objective function based on distances between random curves to define the emission functions of the HMM (for further details, see Martino A., Guatteri, G. and Paganoni A. M. (2019) <https://mox.polimi.it/publication-results/?id=805&tipo=add_qmox>).

Version: 1.0
Depends: R (≥ 3.6.0)
Imports: graphics, stats, gmfd, mvtnorm, roahd
Suggests: knitr, rmarkdown
Published: 2019-09-04
Author: Andrea Martino [aut, cre], Giuseppina Guatteri [aut], Anna Maria Paganoni [aut]
Maintainer: Andrea Martino <andrea.martino at polimi.it>
License: GPL-3
NeedsCompilation: no
CRAN checks: hmmhdd results

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Reference manual: hmmhdd.pdf
Package source: hmmhdd_1.0.tar.gz
Windows binaries: r-devel: hmmhdd_1.0.zip, r-release: hmmhdd_1.0.zip, r-oldrel: not available
OS X binaries: r-release: hmmhdd_1.0.tgz, r-oldrel: not available

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