GenHMM1d: 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) <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

Documentation:

Reference manual: GenHMM1d.pdf

Downloads:

Package source: GenHMM1d_0.1.0.tar.gz
Windows binaries: r-devel: GenHMM1d_0.1.0.zip, r-release: GenHMM1d_0.1.0.zip, r-oldrel: GenHMM1d_0.1.0.zip
macOS binaries: r-release (arm64): GenHMM1d_0.1.0.tgz, r-release (x86_64): GenHMM1d_0.1.0.tgz, r-oldrel: GenHMM1d_0.1.0.tgz

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