rmcmc
The facts below are what the maker publishes about rmcmc, checked and dated. Our editors have not written about it yet.
The facts
Each one read from rmcmc's own pages, with the date it was checked.
What it does
- Adaptation
- During warm-up, adapters can tune proposal scale and shape; proposal parameters remain fixed during the main chain iterations.checked 12 Oct 2026
- BridgeStan integration
- If BridgeStan’s R interface is installed, it can be used to specify the target distribution for sampling.checked 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.checked 12 Oct 2026
- Default proposal
- Chains use the Barker proposal by default, a gradient-based method inspired by the Barker accept-reject rule.checked 12 Oct 2026
- Installation
- The site gives CRAN installation instructions and instructions for installing the development version from GitHub.checked 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.checked 5 Oct 2026
- License
- The site identifies the package license as MIT.checked 12 Oct 2026
- Maintainer
- Matthew M. Graham is listed as author and maintainer.checked 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.checked 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.checked 5 Oct 2026
- Proposals
- It supports Metropolis-Hastings transitions with random-walk, Langevin, Hamiltonian, and Barker proposals.checked 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.checked 12 Oct 2026
- Sampling methods
- It supports Metropolis-Hastings transitions with random-walk, Langevin, Hamiltonian, and Barker proposals; Barker is the default.checked 5 Oct 2026
- Stan integration
- The package can use Stan models through BridgeStan’s R interface when that interface is installed.checked 5 Oct 2026
- Stan workflow
- The documentation includes an article on interfacing with Stan models via BridgeStan.checked 12 Oct 2026
- Support
- The site links to GitHub to report bugs and browse the source code.checked 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.checked 5 Oct 2026
- Warm-up adaptation
- During warm-up, adapters can tune proposal scale and shape.checked 5 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
