Fit and Predict a Gaussian Process Model with (Time-Series) Binary Response R package binaryGP version 0.2 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>. Software 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 Chih-Li Sung <iamdfchile@gmail.com> Comprehensive R Archive Network (CRAN) Chih-Li Sung GPL-2 GPL-3 2017-09-19 application/tgz https://CRAN.R-project.org/package=binaryGP doi:10.32614/CRAN.package.binaryGP