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adaptMCMC: Implementation of a Generic Adaptive Monte Carlo Markov ChainSampler

Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) <doi:10.1007/s11222-011-9269-5> and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.

Version:1.5
Depends:R (≥ 2.14.1), parallel,coda,Matrix
Imports:ramcmc
Published:2024-01-29
DOI:10.32614/CRAN.package.adaptMCMC
Author:Andreas Scheidegger,,
Maintainer:Andreas Scheidegger <andreas.scheidegger at eawag.ch>
BugReports:https://github.com/scheidan/adaptMCMC/issues
License:GPL-2 |GPL-3 [expanded from: GPL (≥ 2)]
URL:https://github.com/scheidan/adaptMCMC
NeedsCompilation:no
CRAN checks:adaptMCMC results

Documentation:

Reference manual:adaptMCMC.html ,adaptMCMC.pdf

Downloads:

Package source: adaptMCMC_1.5.tar.gz
Windows binaries: r-devel:adaptMCMC_1.5.zip, r-release:adaptMCMC_1.5.zip, r-oldrel:adaptMCMC_1.5.zip
macOS binaries: r-release (arm64):adaptMCMC_1.5.tgz, r-oldrel (arm64):adaptMCMC_1.5.tgz, r-release (x86_64):adaptMCMC_1.5.tgz, r-oldrel (x86_64):adaptMCMC_1.5.tgz
Old sources: adaptMCMC archive

Reverse dependencies:

Reverse depends:EpiILM,selectiveInference
Reverse imports:ConsReg,POUMM
Reverse suggests:GUTS

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=adaptMCMCto link to this page.


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