DESCRIPTION

Package: rmcmc
Title: Robust Markov Chain Monte Carlo Methods
Version: 0.0.0.9000
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) 
    <https://doi.org/10.1111/rssb.12482>, is a gradient-based MCMC algorithm
    inspired by the Barker accept-reject rule, which combines the robustness of
    simpler MCMC schemes such as random-walk Metropolis with the efficiency of
    gradient-based algorithms such as Metropolis adjusted Langevin algorithm.
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
Suggests:
    bridgestan (>= 2.5.0),
    knitr,
    posterior,
    progress,
    ramcmc,
    rmarkdown,
    testthat (>= 3.0.0)
Config/testthat/edition: 3
URL: https://github.com/UCL/rmcmc, http://github-pages.ucl.ac.uk/rmcmc/
BugReports: https://github.com/UCL/rmcmc/issues
Imports: 
    Matrix,
    rlang,
    withr
Remotes: 
    roualdes/bridgestan/R
VignetteBuilder: knitr