rjmcmc: Reversible-Jump MCMC Using Post-Processing
Version 0.4.1

Performs reversible-jump Markov chain Monte Carlo (Green, 1995) , specifically the restriction introduced by Barker & Link (2013) . 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.

Package details

AuthorNick Gelling [aut, cre], Matthew R. Schofield [aut], Richard J. Barker [aut]
Date of publication2018-06-27 09:33:37 UTC
MaintainerNick Gelling <[email protected]>
Package repositoryView on CRAN
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rjmcmc documentation built on June 30, 2018, 5:04 p.m.