Goodness-of-Fit for Univariate Hidden Markov Models

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) .


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install.packages("GenHMM1d")

0.1.0 by Bouchra R. Nasri, 5 months ago


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


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


Documentation:   PDF Manual  


GPL-3 license


Imports actuar, EnvStats, extraDistr, ggplot2, matrixcalc, parallel, reshape2, rmutil, ssdtools, VaRES, VGAM

Depends on doParallel, foreach, stats

Suggests gamlss.dist, GeneralizedHyperbolic, gld, GLDEX, sgt, skewt, sn, stabledist


See at CRAN