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Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.
| Version: | 1.1.1 |
| Depends: | R (≥ 3.5.0), doParallel, parallel, foreach, stats |
| Published: | 2023-07-08 |
| Author: | Bouchra R. Nasri [aut, cre, cph], Bruno N Remillard [aut, ctb, cph] |
| Maintainer: | Bouchra R. Nasri <bouchra.nasri at umontreal.ca> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | GaussianHMM1d results |
| Reference manual: | GaussianHMM1d.pdf |
| Package source: | GaussianHMM1d_1.1.1.tar.gz |
| Windows binaries: | r-devel: GaussianHMM1d_1.1.1.zip, r-release: GaussianHMM1d_1.1.1.zip, r-oldrel: GaussianHMM1d_1.1.1.zip |
| macOS binaries: | r-release (arm64): GaussianHMM1d_1.1.1.tgz, r-oldrel (arm64): GaussianHMM1d_1.1.1.tgz, r-release (x86_64): GaussianHMM1d_1.1.1.tgz, r-oldrel (x86_64): GaussianHMM1d_1.1.1.tgz |
| Old sources: | GaussianHMM1d archive |
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