GaussianHMM1d: Inference, Goodness-of-Fit and Forecast for Univariate Gaussian Hidden Markov Models

Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit 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 Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.

Version: 1.1.1
Depends: R (≥ 3.5.0), doParallel, parallel, foreach, stats
Published: 2023-07-08
DOI: 10.32614/CRAN.package.GaussianHMM1d
Author: Bouchra R. Nasri [aut, cre, cph], Bruno N Remillard [aut, ctb, cph]
Maintainer: Bouchra R. Nasri <bouchra.nasri at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: GaussianHMM1d results


Reference manual: GaussianHMM1d.pdf


Package source: GaussianHMM1d_1.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): GaussianHMM1d_1.1.1.tgz, r-oldrel (arm64): GaussianHMM1d_1.1.1.tgz, r-release (x86_64): GaussianHMM1d_1.1.1.tgz, r-oldrel (x86_64): GaussianHMM1d_1.1.1.tgz
Old sources: GaussianHMM1d archive


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