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) .


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("GaussianHMM1d")

1.1.2 by Bouchra R. Nasri, 2 years ago


Browse source code at https://github.com/cran/GaussianHMM1d


Authors: Bouchra R. Nasri [aut, cre, cph] , Bruno N Remillard [aut, ctb, cph]


Documentation:   PDF Manual  


GPL (>= 2) license


Depends on doParallel, parallel, foreach, stats


See at CRAN