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: 1.0.1
Depends: foreach, doParallel, parallel
Published: 2019-03-07
Author: Bouchra R. Nasri and Bruno N. Remillard
Maintainer: Bouchra Nasri <bouchra.nasri at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: GaussianHMM1d results


Reference manual: GaussianHMM1d.pdf


Package source: GaussianHMM1d_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): GaussianHMM1d_1.0.1.tgz, r-oldrel (arm64): GaussianHMM1d_1.0.1.tgz, r-release (x86_64): GaussianHMM1d_1.0.1.tgz, r-oldrel (x86_64): GaussianHMM1d_1.0.1.tgz


Please use the canonical form to link to this page.