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 gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	GaussianHMM1d results

Documentation:

Reference manual:

GaussianHMM1d.pdf

Downloads:

Package source:	GaussianHMM1d_1.0.1.tar.gz
Windows binaries:	r-devel: GaussianHMM1d_1.0.1.zip, r-release: GaussianHMM1d_1.0.1.zip, r-oldrel: GaussianHMM1d_1.0.1.zip
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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=GaussianHMM1d to link to this page.