GaussianHMM1d: Inference, Goodness-of-Fit and Forecast for Univariate Gaussian
Hidden Markov Models
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: |
1.1.2 |
| Depends: |
R (≥ 3.5.0), doParallel, parallel, foreach, stats |
| Published: |
2025-02-05 |
| 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 |
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