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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 |
Reference manual: | adaptMCMC.pdf |
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 depends: | EpiILM, selectiveInference |
Reverse imports: | ConsReg, POUMM |
Reverse suggests: | GUTS |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.