Package: rjmcmc 0.4.5
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>.
Authors:
rjmcmc_0.4.5.tar.gz
rjmcmc_0.4.5.zip(r-4.5)rjmcmc_0.4.5.zip(r-4.4)rjmcmc_0.4.5.zip(r-4.3)
rjmcmc_0.4.5.tgz(r-4.4-any)rjmcmc_0.4.5.tgz(r-4.3-any)
rjmcmc_0.4.5.tar.gz(r-4.5-noble)rjmcmc_0.4.5.tar.gz(r-4.4-noble)
rjmcmc_0.4.5.tgz(r-4.4-emscripten)rjmcmc_0.4.5.tgz(r-4.3-emscripten)
rjmcmc.pdf |rjmcmc.html✨
rjmcmc/json (API)
| # Install 'rjmcmc' in R: |
| install.packages('rjmcmc', repos = c('https://nickgelling.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:0cc1714f63. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 31 2024 |
| R-4.5-win | NOTE | Oct 31 2024 |
| R-4.5-linux | NOTE | Oct 31 2024 |
| R-4.4-win | NOTE | Oct 31 2024 |
| R-4.4-mac | NOTE | Oct 31 2024 |
| R-4.3-win | NOTE | Oct 31 2024 |
| R-4.3-mac | NOTE | Oct 31 2024 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Automatic Differentiation Using Madness | adiff |
| Perform Post-Processing Using Default Bijections | defaultpost |
| Define Function To Sample From MCMC Output | getsampler |
| The rjmcmc Package | rjmcmc-package rjmcmc |
| Perform Reversible-Jump MCMC Post-Processing | rjmcmcpost |
| Methods for the rj Class | plot.rj print.rj rjmethods summary.rj |
