Nothing
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>.
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 | 1.1.1 |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.