Inference, goodness-of-fit tests, and predictions for continuous and discrete univariate Hidden Markov Models (HMM). The goodness-of-fit test 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 Nasri et al (2020) <doi:10.1029/2019WR025122>.
| Version: | 0.1.0 |
| Depends: | doParallel, foreach, stats |
| Imports: | actuar, EnvStats, extraDistr, ggplot2, matrixcalc, parallel, reshape2, rmutil, ssdtools, VaRES, VGAM |
| Suggests: | gamlss.dist, GeneralizedHyperbolic, gld, GLDEX, sgt, skewt, sn, stabledist |
| Published: | 2021-01-21 |
| Author: | Bouchra R. Nasri [aut, cre, cph], Mamadou Yamar Thioub [aut, cph] |
| Maintainer: | Bouchra R. Nasri <bouchra.nasri at umontreal.ca> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | GenHMM1d results |
| Reference manual: | GenHMM1d.pdf |
| Package source: | GenHMM1d_0.1.0.tar.gz |
| Windows binaries: | r-devel: GenHMM1d_0.1.0.zip, r-release: GenHMM1d_0.1.0.zip, r-oldrel: GenHMM1d_0.1.0.zip |
| macOS binaries: | r-release: GenHMM1d_0.1.0.tgz, r-oldrel: GenHMM1d_0.1.0.tgz |
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