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 DOI: 10.32614/CRAN.package.GenHMM1d 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=GenHMM1d to link to this page.
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