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>.

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Description file content

Package
rjmcmc
Type
Package
Title
Reversible-Jump MCMC Using Post-Processing
Version
0.4.5
Date
2019-07-07
Description
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) .
License
GPL-3
Depends
madness, R (>= 3.2.0)
Imports
utils, coda, mvtnorm
Suggests
FSAdata
RoxygenNote
6.1.0
LazyData
TRUE
NeedsCompilation
no
Packaged
2019-07-07 00:02:12 UTC; rachaelyoung
Author
Nick Gelling [aut, cre], Matthew R. Schofield [aut], Richard J. Barker [aut]
Maintainer
Nick Gelling
Repository
CRAN
Date/Publication
2019-07-09 14:20:02 UTC

install.packages('rjmcmc')

0.4.5

9 days ago

Nick Gelling

GPL-3

Depends on

madness, R (>= 3.2.0)

Imports

utils, coda, mvtnorm

Suggests

FSAdata

Discussions