Nothing
binaryGP: Fit and Predict a Gaussian Process Model with (Time-Series) Binary Response
Allows the estimation and prediction for binary Gaussian process model. The mean function can be assumed to have time-series structure. The estimation methods for the unknown parameters are based on penalized quasi-likelihood/penalized quasi-partial likelihood and restricted maximum likelihood. The predicted probability and its confidence interval are computed by Metropolis-Hastings algorithm. More details can be seen in Sung et al (2017) <arXiv:1705.02511>.
Getting started
Browse package contents
Package details |
|
|---|---|
| Author | Chih-Li Sung |
| Maintainer | Chih-Li Sung <iamdfchile@gmail.com> |
| License | GPL-2 | GPL-3 |
| Version | 0.2 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the binaryGP package in your browser
Any scripts or data that you put into this service are public.
