On the recordRead 12 Oct 202619 facts
rmcmc
On the record
- Adaptation
- During warm-up, adapters can tune proposal scale and shape; proposal parameters remain fixed during the main chain iterations.github-pages.ucl.ac.uk · read 12 Oct 2026
- BridgeStan integration
- If BridgeStan’s R interface is installed, it can be used to specify the target distribution for sampling.github-pages.ucl.ac.uk · read 12 Oct 2026
- Custom noise
- The Barker proposal accepts a custom auxiliary-noise sampling function, and the package provides a convenience function for bimodal noise.github-pages.ucl.ac.uk · read 12 Oct 2026
- Default proposal
- Chains use the Barker proposal by default, a gradient-based method inspired by the Barker accept-reject rule.github-pages.ucl.ac.uk · read 12 Oct 2026
- Installation
- The site gives CRAN installation instructions and instructions for installing the development version from GitHub.github-pages.ucl.ac.uk · read 12 Oct 2026
- Intended use
- The package is for users who want to define a target distribution and sample a Markov chain, with a documented example fitting a model to neuroscience data using Stan.github-pages.ucl.ac.uk · read 5 Oct 2026
- License
- The site identifies the package license as MIT.github-pages.ucl.ac.uk · read 12 Oct 2026
- Maintainer
- Matthew M. Graham is listed as author and maintainer.github-pages.ucl.ac.uk · read 12 Oct 2026
- Other integrations
- The package can use ramcmc for robust adaptive Metropolis adaptation, and its chain output can be passed to posterior for summaries and convergence diagnostics.github-pages.ucl.ac.uk · read 12 Oct 2026
- Output
- Sampling returns the final chain state, main-iteration traces, and transition statistics; warm-up traces and statistics are available when trace_warm_up is enabled.github-pages.ucl.ac.uk · read 5 Oct 2026
- Proposals
- It supports Metropolis-Hastings transitions with random-walk, Langevin, Hamiltonian, and Barker proposals.github-pages.ucl.ac.uk · read 12 Oct 2026
- Purpose
- rmcmc is an R package for simulating Markov chains to compute MCMC estimates for target distributions on real-valued vector spaces.github-pages.ucl.ac.uk · read 12 Oct 2026
- Sampling methods
- It supports Metropolis-Hastings transitions with random-walk, Langevin, Hamiltonian, and Barker proposals; Barker is the default.github-pages.ucl.ac.uk · read 5 Oct 2026
- Stan integration
- The package can use Stan models through BridgeStan’s R interface when that interface is installed.github-pages.ucl.ac.uk · read 5 Oct 2026
- Stan workflow
- The documentation includes an article on interfacing with Stan models via BridgeStan.github-pages.ucl.ac.uk · read 12 Oct 2026
- Support
- The site links to GitHub to report bugs and browse the source code.github-pages.ucl.ac.uk · read 12 Oct 2026
- Target distributions
- The sampling function accepts a log-density formula, a list of density and gradient functions, or a BridgeStan StanModel when BridgeStan is installed.github-pages.ucl.ac.uk · read 5 Oct 2026
- Warm-up adaptation
- During warm-up, adapters can tune proposal scale and shape.github-pages.ucl.ac.uk · read 5 Oct 2026
