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) <doi:10.48550/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
DOI: 10.32614/CRAN.package.binaryGP
Author: Chih-Li Sung
Maintainer: Chih-Li Sung <iamdfchile at>
License: GPL-2 | GPL-3
NeedsCompilation: yes
CRAN checks: binaryGP results


Reference manual: binaryGP.pdf


Package source: binaryGP_0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): binaryGP_0.2.tgz, r-oldrel (arm64): binaryGP_0.2.tgz, r-release (x86_64): binaryGP_0.2.tgz, r-oldrel (x86_64): binaryGP_0.2.tgz


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