c(person("George", "Vega Yon", email = "g.vegayon@gmail.com", role =
c("aut", "cre"), comment = c(ORCID = "0000-0002-3171-0844")),
person("Paul", "Marjoram", email = "pmarjora@usc.edu", role = c("ctb",
"ths"), comment = c(ORCID = "0000-0003-0824-7449")), person("National
Cancer Institute (NCI)", role = "fnd", comment = "Grant Number
5P01CA196569-02"), person("Fabian", "Scheipl", role = "rev", comment =
c(what = "JOSS reviewer", ORCID="0000-0001-8172-3603")) )
Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC)
framework for implementing Metropolis-Hastings algorithm in a modular way
allowing users to specify automatic convergence checker, personalized
transition kernels, and out-of-the-box multiple MCMC chains using
parallel computing. Most of the methods implemented in this package can
be found in Brooks et al. (2011, ISBN 9781420079425).
covr, mvtnorm, knitr, rmarkdown, mcmc, tinytest
George Vega Yon [aut, cre] (<https://orcid.org/0000-0002-3171-0844>),
Paul Marjoram [ctb, ths] (<https://orcid.org/0000-0003-0824-7449>),
National Cancer Institute (NCI) [fnd] (Grant Number 5P01CA196569-02),
Fabian Scheipl [rev] (JOSS reviewer,
<https://orcid.org/0000-0001-8172-3603>)

Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC)
framework for implementing Metropolis-Hastings algorithm in a modular way
allowing users to specify automatic convergence checker, personalized
transition kernels, and out-of-the-box multiple MCMC chains using
parallel computing. Most of the methods implemented in this package can
be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods
included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5>
Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5>
Robust Adaptive Metropolis, and Thawornwattana et
al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.
covr, knitr, rmarkdown, mcmc, tinytest, mvtnorm, adaptMCMC

parallel, coda, stats, methods, MASS, Matrix

covr, knitr, rmarkdown, mcmc, tinytest, mvtnorm,