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.

Version: 0.4.0
Depends: madness, R (≥ 3.2.0)
Imports: utils, coda, mvtnorm
Suggests: knitr, FSAdata
Published: 2017-11-02
Author: Nick Gelling [aut, cre], Matthew R. Schofield [aut], Richard J. Barker [aut]
Maintainer: Nick Gelling <ngelling at maths.otago.ac.nz>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: rjmcmc results


Reference manual: rjmcmc.pdf
Vignettes: R Package rjmcmc: The Calculation of Posterior Model Probabilities from MCMC Output
Package source: rjmcmc_0.4.0.tar.gz
Windows binaries: r-devel: rjmcmc_0.4.0.zip, r-release: rjmcmc_0.4.0.zip, r-oldrel: rjmcmc_0.4.0.zip
OS X El Capitan binaries: r-release: rjmcmc_0.4.0.tgz
OS X Mavericks binaries: r-oldrel: rjmcmc_0.4.0.tgz
Old sources: rjmcmc archive


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