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

Downloads:

Reference manual: binaryGP.pdf
Package source: binaryGP_0.2.tar.gz
Windows binaries: r-devel: binaryGP_0.2.zip, r-devel-gcc8: binaryGP_0.2.zip, r-release: binaryGP_0.2.zip, r-oldrel: binaryGP_0.2.zip
OS X binaries: r-release: binaryGP_0.2.tgz, r-oldrel: binaryGP_0.2.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=binaryGP to link to this page.