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>.
| Version: |
0.2 |
| Depends: |
R (≥ 2.14.1) |
| Imports: |
Rcpp (≥ 0.12.0), lhs (≥ 0.10), logitnorm (≥ 0.8.29), nloptr (≥ 1.0.4), GPfit (≥ 1.0-0), stats, graphics, utils, methods |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Published: |
2017-09-19 |
| Author: |
Chih-Li Sung |
| Maintainer: |
Chih-Li Sung <iamdfchile at gmail.com> |
| License: |
GPL-2 | GPL-3 |
| NeedsCompilation: |
yes |
| CRAN checks: |
binaryGP results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=binaryGP
to link to this page.