Package: rmcmc
Title: Robust Markov Chain Monte Carlo Methods
Version: 0.1.2
Authors@R: c(
    person(c("Matthew", "M."), "Graham", , "m.graham@ucl.ac.uk",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-9104-7960")),
    person("Samuel", "Livingstone", role = "aut",
           comment = c(ORCID = "0000-0002-7277-086X")),
    person("University College London", role = "cph"),
    person("Engineering and Physical Sciences Research Council", role = "fnd")
  )
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
Suggests: bridgestan (>= 2.5.0), knitr, posterior, progress, ramcmc,
        rmarkdown, testthat (>= 3.0.0)
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,
        gridExtra
URL: https://github.com/UCL/rmcmc, http://github-pages.ucl.ac.uk/rmcmc/
BugReports: https://github.com/UCL/rmcmc/issues
Imports: Matrix, rlang, withr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-10-17 11:54:56 UTC; matt
Author: Matthew M. Graham [aut, cre] (ORCID:
    <https://orcid.org/0000-0001-9104-7960>),
  Samuel Livingstone [aut] (ORCID:
    <https://orcid.org/0000-0002-7277-086X>),
  University College London [cph],
  Engineering and Physical Sciences Research Council [fnd]
Maintainer: Matthew M. Graham <m.graham@ucl.ac.uk>
Repository: CRAN
Date/Publication: 2025-10-17 22:10:02 UTC
Built: R 4.6.0; ; 2026-04-26 01:46:31 UTC; unix
