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rjmcmc  

Reversible-Jump MCMC Using Post-Processing
View on CRAN: Click here


Download and install rjmcmc package within the R console
Install from CRAN:
install.packages("rjmcmc")

Install from Github:
library("remotes")
install_github("cran/rjmcmc")

Install by package version:
library("remotes")
install_version("rjmcmc", "0.4.5")



Attach the package and use:
library("rjmcmc")
Maintained by
Nick Gelling
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-03-20
Latest Update: 2019-07-09
Description:
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>.
How to cite:
Nick Gelling (2017). rjmcmc: Reversible-Jump MCMC Using Post-Processing. R package version 0.4.5, https://cran.r-project.org/web/packages/rjmcmc
Previous versions and publish date:
0.2.2 (2017-03-20 06:22), 0.3.01 (2017-08-03 07:18), 0.3.2 (2017-08-11 06:02), 0.4.0 (2017-11-02 05:38), 0.4.1 (2018-06-27 11:33), 0.4.2 (2018-09-29 07:00), 0.4.3 (2018-10-05 00:50), 0.4.4 (2019-03-02 09:00)
Other packages that cited rjmcmc R package
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Other R packages that rjmcmc depends, imports, suggests or enhances
Functions, R codes and Examples using the rjmcmc R package
Some associated functions: adiff . defaultpost . getsampler . rjmcmc . rjmcmcpost . rjmethods . 
Some associated R codes: adiff.R . class.R . defaultpost.R . functionlist.R . getsampler.R . rjmcmcpost.R .  Full rjmcmc package functions and examples
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