Package: adaptMCMC 1.5
adaptMCMC: Implementation of a Generic Adaptive Monte Carlo Markov Chain Sampler
Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) <doi:10.1007/s11222-011-9269-5> and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.
Authors:
adaptMCMC_1.5.tar.gz
adaptMCMC_1.5.zip(r-4.5)adaptMCMC_1.5.zip(r-4.4)adaptMCMC_1.5.zip(r-4.3)
adaptMCMC_1.5.tgz(r-4.4-any)adaptMCMC_1.5.tgz(r-4.3-any)
adaptMCMC_1.5.tar.gz(r-4.5-noble)adaptMCMC_1.5.tar.gz(r-4.4-noble)
adaptMCMC_1.5.tgz(r-4.4-emscripten)adaptMCMC_1.5.tgz(r-4.3-emscripten)
adaptMCMC.pdf |adaptMCMC.html✨
adaptMCMC/json (API)
| # Install 'adaptMCMC' in R: |
| install.packages('adaptMCMC', repos = c('https://scheidan.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/scheidan/adaptmcmc/issues
Last updated 10 months agofrom:b9d2c84673. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 25 2024 |
| R-4.5-win | OK | Oct 25 2024 |
| R-4.5-linux | OK | Oct 25 2024 |
| R-4.4-win | OK | Oct 25 2024 |
| R-4.4-mac | OK | Oct 25 2024 |
| R-4.3-win | OK | Oct 25 2024 |
| R-4.3-mac | OK | Oct 25 2024 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Generic adaptive Monte Carlo Markov Chain sampler | adaptMCMC-package adaptMCMC |
| Converts chain(s) into 'coda' objects. | convert.to.coda |
| (Adaptive) Metropolis Sampler | MCMC |
| Add samples to an existing chain. | MCMC.add.samples |
| Parallel computation of MCMC() | MCMC.parallel |
