R Observatory: Metrics for rmcmc version 0.1.2

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

rmcmc

0.1.2

Robust Markov Chain Monte Carlo Methods

0packages depend

6.3Kdownloads / year

89.6%test coverage

13/13checks pass

Overview

About

Maintained by Matthew M. GrahamFirst published 2025-02-042 releasesCRAN page ↗GitHub ↗

Functions for simulating Markov chains using the Barker proposal to compute Markov chain Monte Carlo (MCMC) estimates of expectations with respect to a target distribution on a real-valued vector space. The Barker proposal, described in Livingstone and Zanella (2022) doi:10.1111/rssb.12482, is a gradient-based MCMC algorithm inspired by the Barker accept-reject rule. It combines the robustness of simpler MCMC schemes, such as random-walk Metropolis, with the efficiency of gradient-based methods, such as the Metropolis adjusted Langevin algorithm. The key function provided by the package is sample_chain(), which allows sampling a Markov chain with a specified target distribution as its stationary distribution. The chain is sampled by generating proposals and accepting or rejecting them using a Metropolis-Hasting acceptance rule. During an initial warm-up stage, the parameters of the proposal distribution can be adapted, with adapters available to both: tune the scale of the proposals by coercing the average acceptance rate to a target value; tune the shape of the proposals to match covariance estimates under the target distribution. As well as the default Barker proposal, the package also provides implementations of alternative proposal distributions, such as (Gaussian) random walk and Langevin proposals. Optionally, if 'BridgeStan's R interface https://roualdes.us/bridgestan/latest/languages/r.html, available on GitHub https://github.com/roualdes/bridgestan, is installed, then 'BridgeStan' can be used to specify the target distribution to sample from.

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("rmcmc", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("UCL/rmcmc")

Development version from UCL/rmcmc. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-rmcmc

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-rmcmc

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-rmcmc

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed).

Health

CRAN checks

13OK

Slowest check: 2.8 min · r-oldrel-windows-x86_64

Code health

Yes

Tests · ratio 0.95

89.6%

Coverage · measured lines

100%

Documentation · exports

3

Dependencies · direct

Check history

OK2026-08-05
13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
NOTE2026-08-01
12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
OK2026-04-25
12 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
NOTE2026-03-10
11 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMEYes · 438 wordsVignettesYes · dynamicpkgdown siteNoNEWSYes · 100% structuredCode of conductNoContributing guideNo

Examples that run: 100%
Documented parameters: 97%
Return-value docs: 100%
References docs: 36%

Downloads

6.3K

CRAN downloads in the past year

Rank #7,757 · ~17/day · ~527/mo

Releases R versions

Daily download trend is not available in this view yet.

24730 days

1.1K90 days

6.3K1 year

Compare downloads with other packages →

Also on112 r2u22 autocran

Repository

GitHub ↗ ucl/rmcmc

8Stars

8Forks

9Open issues

4Open PRs

2Releases

95Commits

3Contributors

approximate-inferencemcmcr

95 commits · Last activity 2026-08-17 · 0% stars, 30d

Stars over time

2025-02-18 · 52026-07-07 · 8

Repository practices

Upstream repositoryBeta

7 development-tooling and community-health practices detected across 5 families in the upstream repository

Checks run against github.com/ucl/rmcmc on 2026-08-16.

Continuous integration (1)

GitHub Actions

CRAN release process (1)

cran-comments.md

Docs source (1)

README.Rmd

Automation and maintenance (2)

Dependabotpre-commit

Show all practices

Lint, format, editor (2)

lintrRStudio project

How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies

9 external dependencies (excludes base and recommended)

Depends (0)

none

Imports (3)

Matrixrlang withr

LinkingTo (0)

none

Suggests (7)

bridgestan knitr posterior progress ramcmc rmarkdown testthat

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (4)

Maintainer (1)

Matthew M. Graham

Author, Maintainer

Authors (2)

Matthew M. Graham

Author, Maintainer

Samuel Livingstone

Author

Copyright holders (1)

University College London

Copyright holder

Funders (1)

Engineering and Physical Sciences Research Council

Funder

Listed in earlier versions (1)

Matthew M.

no longer listed · 0.1.1 to 0.1.2

Package Timeline

2 releases. Pick two to compare their code metrics. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
0.1.2Latest
2025-10-18 · current release · diff ↗
R
R 4.5.0 released · 2025-04-11
0.1.1
2025-02-04
R
R 4.4.0 released · 2024-04-24

Package metadata

First published: 2025-02-04
Total releases: 2 / 1 yrs
License: MIT + file LICENSE OSI
Download size: 212 KB
Installed size: not tracked yet
With dependencies: not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("rmcmc")

Format

Graham, M. M., Engineering and Physical Sciences Research Council, University College London, & Livingstone, S. (2025). rmcmc: Robust Markov Chain Monte Carlo Methods (Version 0.1.2) [Computer software]. https://doi.org/10.32614/CRAN.package.rmcmc

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for rmcmc version 0.1.2 [Data set]. HJJB, LLC. Data release v2026-08-21. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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