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Estimates hidden Markov models from the family of Cholesky-decomposed Gaussian hidden Markov models (CDGHMM) under various missingness schemes. This family improves upon estimation of traditional Gaussian HMMs by introducing parsimony, as well as, controlling for dropped out observations and non-random missingness. See Neal, Sochaniwsky and McNicholas (2024) <doi:10.1007/s11222-024-10462-0>.
Version: | 0.1.1 |
Depends: | R (≥ 3.5.0) |
Imports: | MASS, mvtnorm, ramify, cluster |
Published: | 2024-11-01 |
DOI: | 10.32614/CRAN.package.CDGHMM |
Author: | Mackenzie R. Neal [aut, cre], Alexa A. Sochaniwsky [aut], Paul D. McNicholas [aut] |
Maintainer: | Mackenzie R. Neal <nealm6 at mcmaster.ca> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | no |
Citation: | CDGHMM citation info |
CRAN checks: | CDGHMM results |
Reference manual: | CDGHMM.pdf |
Package source: | CDGHMM_0.1.1.tar.gz |
Windows binaries: | r-devel: CDGHMM_0.1.1.zip, r-release: CDGHMM_0.1.1.zip, r-oldrel: CDGHMM_0.1.1.zip |
macOS binaries: | r-release (arm64): CDGHMM_0.1.1.tgz, r-oldrel (arm64): CDGHMM_0.1.1.tgz, r-release (x86_64): CDGHMM_0.1.1.tgz, r-oldrel (x86_64): CDGHMM_0.1.1.tgz |
Old sources: | CDGHMM archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.