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.
- Version1.1.2
- R versionR (≥ 3.5.0)
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- Last release02/05/2025
Team
Bouchra R. Nasri
MaintainerShow author detailsBruno N Remillard
Show author detailsRolesAuthor, Contributor, Copyright holder
Insights
Last 30 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends2 packages