rjmcmc (0.4.5)

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Reversible-Jump MCMC Using Post-Processing.


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. For a detailed description of the package, see Gelling, Schofield & Barker (2019) .

Maintainer: Nick Gelling
Author(s): Nick Gelling [aut, cre], Matthew R. Schofield [aut], Richard J. Barker [aut]

License: GPL-3

Uses: coda, madness, mvtnorm, FSAdata

Released 7 months ago.

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