adaptMCMC: Implementation of a Generic Adaptive Monte Carlo Markov Chain Sampler

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>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: adaptMCMC results


Reference manual: adaptMCMC.pdf


Package source: adaptMCMC_1.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


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