Package: MCMChybridGP 7.0.1

Mark J. Fielding

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).

Authors:Mark J. Fielding [aut, cre]

MCMChybridGP_7.0.1.tar.gz
MCMChybridGP_7.0.1.tar.gz(r-4.7-arm64)MCMChybridGP_7.0.1.tar.gz(r-4.7-x86_64)MCMChybridGP_7.0.1.tar.gz(r-4.6-arm64)MCMChybridGP_7.0.1.tar.gz(r-4.6-x86_64)
MCMChybridGP_7.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
MCMChybridGP/json (API)

# Install 'MCMChybridGP' in R:
install.packages('MCMChybridGP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

1.00 score 42 downloads 4 exports 2 dependencies

Last updated from:4c25c00e3c. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK127
linux-devel-x86_64OK105
source / vignettesOK173
linux-release-arm64OK126
linux-release-x86_64OK113
wasm-releaseOK136

Exports:generateX0GProcesshybrid.explorehybrid.sample

Dependencies:MASSRcpp