GenHMM1d: Goodness-of-Fit for Zero-Inflated Univariate Hidden Markov
Models
Inference, goodness-of-fit tests, and predictions for continuous and discrete univariate Hidden Markov Models (HMM), including zero-inflated distributions. 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: |
0.2.1 |
| Depends: |
R (≥ 3.5.0), doParallel, parallel, foreach |
| Imports: |
ggplot2, stats, matrixcalc, reshape2, rmutil, VaRES, VGAM, EnvStats, GLDEX, GeneralizedHyperbolic, actuar, extraDistr, gamlss.dist, sgt, skewt, sn, ssdtools, stabledist |
| Published: |
2025-03-13 |
| Author: |
Bouchra R. Nasri [aut, cre, cph],
Mamadou Yamar Thioub [aut, cph],
Bruno N. Remillard [aut, cph] |
| Maintainer: |
Bouchra R. Nasri <bouchra.nasri at umontreal.ca> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
GenHMM1d results |
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