rmcmc

Spec sheet

Purpose

rmcmc is an R package for simulating Markov chains to compute MCMC estimates for target distributions on real-valued vector spaces.Read 12 Oct 2026 · github-pages.ucl.ac.uk

Free plan

YesRead 23 Sep 2026 · github-pages.ucl.ac.uk

Support

The site links to GitHub to report bugs and browse the source code.Read 12 Oct 2026 · github-pages.ucl.ac.uk

License

The site identifies the package license as MIT.Read 12 Oct 2026 · github-pages.ucl.ac.uk

Adaptation

During warm-up, adapters can tune proposal scale and shape; proposal parameters remain fixed during the main chain iterations.Read 12 Oct 2026 · github-pages.ucl.ac.uk

BridgeStan integration

If BridgeStan’s R interface is installed, it can be used to specify the target distribution for sampling.Read 12 Oct 2026 · github-pages.ucl.ac.uk

Custom noise

The Barker proposal accepts a custom auxiliary-noise sampling function, and the package provides a convenience function for bimodal noise.Read 12 Oct 2026 · github-pages.ucl.ac.uk

Default proposal

Chains use the Barker proposal by default, a gradient-based method inspired by the Barker accept-reject rule.Read 12 Oct 2026 · github-pages.ucl.ac.uk

Installation

The site gives CRAN installation instructions and instructions for installing the development version from GitHub.Read 12 Oct 2026 · github-pages.ucl.ac.uk

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.Read 5 Oct 2026 · github-pages.ucl.ac.uk

Maintainer

Matthew M. Graham is listed as author and maintainer.Read 12 Oct 2026 · github-pages.ucl.ac.uk

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.Read 12 Oct 2026 · github-pages.ucl.ac.uk

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.Read 5 Oct 2026 · github-pages.ucl.ac.uk

Proposals

It supports Metropolis-Hastings transitions with random-walk, Langevin, Hamiltonian, and Barker proposals.Read 12 Oct 2026 · github-pages.ucl.ac.uk

Sampling methods

It supports Metropolis-Hastings transitions with random-walk, Langevin, Hamiltonian, and Barker proposals; Barker is the default.Read 5 Oct 2026 · github-pages.ucl.ac.uk

Stan integration

The package can use Stan models through BridgeStan’s R interface when that interface is installed.Read 5 Oct 2026 · github-pages.ucl.ac.uk

Stan workflow

The documentation includes an article on interfacing with Stan models via BridgeStan.Read 12 Oct 2026 · github-pages.ucl.ac.uk

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.Read 5 Oct 2026 · github-pages.ucl.ac.uk

Warm-up adaptation

During warm-up, adapters can tune proposal scale and shape.Read 5 Oct 2026 · github-pages.ucl.ac.uk

Where Specifiction places it

• Statistical Analysis Software#106 of 1066.4