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: 1.2
Depends: R (≥ 2.14.1), parallel, coda, Matrix
Published: 2017-06-30
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

Downloads:

Reference manual: adaptMCMC.pdf
Package source: adaptMCMC_1.2.tar.gz
Windows binaries: r-devel: adaptMCMC_1.2.zip, r-release: adaptMCMC_1.2.zip, r-oldrel: adaptMCMC_1.2.zip
OS X El Capitan binaries: r-release: adaptMCMC_1.2.tgz
OS X Mavericks binaries: r-oldrel: adaptMCMC_1.2.tgz
Old sources: adaptMCMC archive

Reverse dependencies:

Reverse imports: MSGARCH, POUMM
Reverse suggests: GUTS

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