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) .


Reference manual

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0.1.0 by Bouchra R. Nasri, 5 months ago

Browse source code at

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