Nothing
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>.
Getting started
Browse package contents
Package details |
|
|---|---|
| Author | Bouchra R. Nasri [aut, cre, cph], Bruno N Remillard [aut, ctb, cph] |
| Maintainer | Bouchra R. Nasri <bouchra.nasri@umontreal.ca> |
| License | GPL (>= 2) |
| Version | 1.1.2 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the GaussianHMM1d package in your browser
Any scripts or data that you put into this service are public.
