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 |