R interface to the OpenBUGS MCMC software
OpenBUGS was developed by Andrew Thomas, Dave Lunn, David Spiegelhalter and Nicky Best.  R interface developed by Uwe Ligges, Sibylle Sturtz, Andrew Gelman, Gregor Gorjanc and Chris Jackson.  Linux port and most recent developments by Chris Jackson.
Fully-interactive R interface to the OpenBUGS software for Bayesian analysis using MCMC sampling.  Runs natively and stably in 32-bit R under Windows.  Versions running on Linux and on 64-bit R under Windows are in "beta" status and less efficient.
OpenBUGS (>= 3.2.1), hence Windows or Linux

OpenBUGS was developed by Andrew Thomas, Dave Lunn, David
Spiegelhalter and Nicky Best.  R interface developed by Uwe
Ligges, Sibylle Sturtz, Andrew Gelman, Gregor Gorjanc and Chris
Jackson.  Linux port and most recent developments by Chris
Jackson.
Fully-interactive R interface to the OpenBUGS software for
Bayesian analysis using MCMC sampling.  Runs natively and
stably in 32-bit R under Windows.  Versions running on Linux
and on 64-bit R under Windows are in "beta" status and less
efficient.
OpenBUGS (>= 3.2.2), hence Windows or Linux

Interface to the 'OpenBUGS' MCMC Software
Fully-interactive R interface to the 'OpenBUGS' software for Bayesian analysis using MCMC sampling.  Runs natively and stably in 32-bit R under Windows.  Versions running on Linux and on 64-bit R under Windows are in "beta" status and less efficient.

The Bayesian Spatial Bradley--Terry Model
c(
person(given = "Rowland",
family = "Seymour",
role = c("aut", "cre"),
email = "rowland.seymour@nottingham.ac.uk",
comment = c(ORCID = "0000-0002-8739-3921")),
person(given = "James",
family = "Briant",
role = c("aut"),
email = "james@briant.co.uk")
)
An implementation of the Bayesian Spatial Bradley--Terry (BSBT) model. It can be used to investigate data sets where judges compared different spatial areas. It constructs a network to describe how the areas are connected, and then places a correlated prior distribution on the quality parameter for each area, based on the network. The package includes MCMC algorithms to estimate the quality parameters. The methodology is published in Seymour et. al. (2020) <arXiv:2010.14128>.
knitr, rmarkdown, testthat, sf, surveillance, spdep,
RColorBrewer, deldir
Rowland Seymour [aut, cre] (<https://orcid.org/0000-0002-8739-3921>),
James Briant [aut]