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: | 1.3 |
| Depends: | R (≥ 2.14.1), parallel, coda, Matrix |
| Published: | 2018-01-14 |
| 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_1.3.tar.gz |
| Windows binaries: | r-devel: adaptMCMC_1.3.zip, r-release: adaptMCMC_1.3.zip, r-oldrel: adaptMCMC_1.3.zip |
| macOS binaries: | r-release: adaptMCMC_1.3.tgz, r-oldrel: adaptMCMC_1.3.tgz |
| Old sources: | adaptMCMC archive |
| Reverse depends: | EpiILM, selectiveInference |
| Reverse imports: | ConsReg, POUMM |
| Reverse suggests: | fmcmc, GUTS |
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