{ "_id": "6a19f1e41d7bb097a09ca61c", "Package": "rmcmc", "Title": "Robust Markov Chain Monte Carlo Methods", "Version": "0.1.2.9000", "Authors@R": "c(\nperson(c(\"Matthew\", \"M.\"), \"Graham\", , \"m.graham@ucl.ac.uk\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-9104-7960\")),\nperson(\"Samuel\", \"Livingstone\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-7277-086X\")),\nperson(\"University College London\", role = \"cph\"),\nperson(\"Engineering and Physical Sciences Research Council\", role = \"fnd\")\n)", "Description": "Functions for simulating Markov chains using the Barker\nproposal to compute Markov chain Monte Carlo (MCMC) estimates\nof expectations with respect to a target distribution on a\nreal-valued vector space. The Barker proposal, described in\nLivingstone and Zanella (2022) , is a\ngradient-based MCMC algorithm inspired by the Barker\naccept-reject rule. It combines the robustness of simpler MCMC\nschemes, such as random-walk Metropolis, with the efficiency of\ngradient-based methods, such as the Metropolis adjusted\nLangevin algorithm. The key function provided by the package is\nsample_chain(), which allows sampling a Markov chain with a\nspecified target distribution as its stationary distribution.\nThe chain is sampled by generating proposals and accepting or\nrejecting them using a Metropolis-Hasting acceptance rule.\nDuring an initial warm-up stage, the parameters of the proposal\ndistribution can be adapted, with adapters available to both:\ntune the scale of the proposals by coercing the average\nacceptance rate to a target value; tune the shape of the\nproposals to match covariance estimates under the target\ndistribution. As well as the default Barker proposal, the\npackage also provides implementations of alternative proposal\ndistributions, such as (Gaussian) random walk and Langevin\nproposals. Optionally, if 'BridgeStan's R interface\n,\navailable on GitHub ,\nis installed, then 'BridgeStan' can be used to specify the\ntarget distribution to sample from.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "Config/testthat/edition": "3", "Config/Needs/bridgestan": "bridgestan=url::https://community.r-multiverse.org/src/contrib/bridgestan_2.7.0.tar.gz", "Config/Needs/check": "any::rcmdcheck", "Config/Needs/coverage": "any::covr, any::xml2", "Config/Needs/lint": "any::lintr, any::cyclocomp", "Config/Needs/style": "any::styler", "Config/Needs/website": "any::pkgdown, ggplot2, bayesplot, rjson, hexbin,\ngridExtra", "URL": "https://github.com/UCL/rmcmc, http://github-pages.ucl.ac.uk/rmcmc/", "BugReports": "https://github.com/UCL/rmcmc/issues", "VignetteBuilder": "knitr", "Config/roxygen2/version": "8.0.0", "Repository": "https://ucl.r-universe.dev", "Date/Publication": "2026-05-29 17:26:42 UTC", "RemoteUrl": "https://github.com/ucl/rmcmc", "RemoteRef": "HEAD", "RemoteSha": "ddd32abc0d42a9bd6da8a512e68436fcca3713cb", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-29 19:53:04 UTC", "User": "root" }, "Author": "Matthew M. Graham [aut, cre] (ORCID:\n),\nSamuel Livingstone [aut] (ORCID:\n),\nUniversity College London [cph],\nEngineering and Physical Sciences Research Council [fnd]", "Maintainer": "Matthew M. 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Graham", "email": "m.graham@ucl.ac.uk", "login": "matt-graham", "mastodon": "@matt@bayes.club", "orcid": "0000-0001-9104-7960", "description": "Research data scientist at UCL", "uuid": 6746980 }, "_registered": true, "_dependencies": [ { "package": "Matrix", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "withr", "role": "Imports" }, { "package": "bridgestan", "version": ">= 2.5.0", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "posterior", "role": "Suggests" }, { "package": "progress", "role": "Suggests" }, { "package": "ramcmc", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" } ], "_owner": "ucl", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-32", "n": 6 }, { "week": "2025-37", "n": 3 }, { "week": "2025-42", "n": 5 }, { "week": "2025-45", "n": 2 }, { "week": "2025-51", "n": 4 }, { "week": "2026-04", "n": 1 }, { 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