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 gmail.com>
License:	GPL-2 | GPL-3
NeedsCompilation:	yes
CRAN checks:	binaryGP results

Documentation:

Reference manual:

binaryGP.pdf

Downloads:

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

Linking:

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