rjmcmc: Reversible-Jump MCMC Using Post-Processing

Performs reversible-jump Markov chain Monte Carlo (Green, 1995) <doi:10.2307/2337340>, specifically the restriction introduced by Barker & Link (2013) <doi:10.1080/00031305.2013.791644>. By utilising a 'universal parameter' space, RJMCMC is treated as a Gibbs sampling problem. Previously-calculated posterior distributions are used to quickly estimate posterior model probabilities. Jacobian matrices are found using automatic differentiation. For a detailed description of the package, see Gelling, Schofield & Barker (2019) <doi:10.1111/anzs.12263>.

Version:	0.4.5
Depends:	madness, R (≥ 3.2.0)
Imports:	utils, coda, mvtnorm
Suggests:	FSAdata
Published:	2019-07-09
DOI:	10.32614/CRAN.package.rjmcmc
Author:	Nick Gelling [aut, cre], Matthew R. Schofield [aut], Richard J. Barker [aut]
Maintainer:	Nick Gelling <nickcjgelling at gmail.com>
License:	GPL-3
NeedsCompilation:	no
Materials:	README
CRAN checks:	rjmcmc results

Documentation:

Reference manual:

rjmcmc.pdf

Downloads:

Package source:	rjmcmc_0.4.5.tar.gz
Windows binaries:	r-devel: rjmcmc_0.4.5.zip, r-release: rjmcmc_0.4.5.zip, r-oldrel: rjmcmc_0.4.5.zip
macOS binaries:	r-release (arm64): rjmcmc_0.4.5.tgz, r-oldrel (arm64): rjmcmc_0.4.5.tgz, r-release (x86_64): rjmcmc_0.4.5.tgz, r-oldrel (x86_64): rjmcmc_0.4.5.tgz
Old sources:	rjmcmc archive

Reverse dependencies:

Reverse imports:

BayesOrdDesign

Linking:

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