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>.

Getting started

Package details

AuthorNick Gelling [aut, cre], Matthew R. Schofield [aut], Richard J. Barker [aut]
MaintainerNick Gelling <nickcjgelling@gmail.com>
Package repositoryView on CRAN
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rjmcmc documentation built on July 9, 2019, 5:03 p.m.