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) .


Reference manual

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install.packages("GenHMM1d")

0.2.8 by Bouchra R. Nasri, a month ago


Browse source code at https://github.com/cran/GenHMM1d


Authors: Bouchra R. Nasri [aut, cre, cph] , Mamadou Yamar Thioub [aut, cph] , Bruno N. Remillard [aut, cph]


Documentation:   PDF Manual  


GPL-3 license


Imports ggplot2, stats, matrixcalc, reshape2, rmutil, VaRES, VGAM, EnvStats, GLDEX, GeneralizedHyperbolic, actuar, extraDistr, sgt, skewt, sn, ssdtools, stabledist

Depends on doParallel, parallel, foreach


See at CRAN