MCMChybridGP: Hybrid Markov Chain Monte Carlo Using Gaussian Processes

Hybrid Markov chain Monte Carlo (MCMC) for sampling from multimodal target distributions when derivatives are unavailable. A Gaussian process approximation is used to emulate derivatives, enabling efficient exploration with parallel tempering. The method is described in Fielding, Nott and Liong (2011) <doi:10.1198/TECH.2010.09195>. The research was carried out as part of the Singapore-Delft Water Alliance Multi-Objective Multi-Reservoir Management programme (R-264-001-272).

Version:	7.0.1
Depends:	R (≥ 4.2.0)
Imports:	MASS, Rcpp
LinkingTo:	Rcpp
Published:	2026-06-24
DOI:	10.32614/CRAN.package.MCMChybridGP
Author:	Mark J. Fielding [aut, cre]
Maintainer:	Mark J. Fielding <mark.fielding at gmx.com>
License:	GPL-2
NeedsCompilation:	yes
CRAN checks:	MCMChybridGP results

Documentation:

Reference manual:

MCMChybridGP.html , MCMChybridGP.pdf

Downloads:

Package source:	MCMChybridGP_7.0.1.tar.gz
Windows binaries:	r-devel: not available, r-release: MCMChybridGP_7.0.1.zip, r-oldrel: not available
macOS binaries:	r-release (arm64): MCMChybridGP_7.0.1.tgz, r-oldrel (arm64): MCMChybridGP_7.0.1.tgz, r-release (x86_64): MCMChybridGP_7.0.1.tgz, r-oldrel (x86_64): MCMChybridGP_7.0.1.tgz
Old sources:	MCMChybridGP archive

Linking:

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