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 |