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.

Package details

AuthorNick Gelling [aut, cre], Matthew R. Schofield [aut], Richard J. Barker [aut]
MaintainerNick Gelling <[email protected]>
LicenseGPL-3
Version0.4.3
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("rjmcmc")

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rjmcmc documentation built on Oct. 5, 2018, 1:03 a.m.