Scalable Rejection Sampling for Bayesian Hierarchical Models
Functions for implementing the Braun and Damien (2015) rejection sampling algorithm for Bayesian hierarchical models.  The algorithm generates posterior samples in parallel, and is scalable when the individual units are conditionally independent.
sparseHessianFD(>= 0.2.0), sparseMVN(>= 0.2.0), mvtnorm,
trustOptim (>= 0.8.5), plyr (>= 1.8), dplyr, testthat, knitr,
R.rsp, MCMCpack

Functions for implementing the Braun and Damien (2015) rejection
sampling algorithm for Bayesian hierarchical models. The algorithm generates
posterior samples in parallel, and is scalable when the individual units are
conditionally independent.
sparseHessianFD(>= 0.3.0), sparseMVN(>= 0.2.0), mvtnorm,
trustOptim (>= 0.8.5), plyr (>= 1.8), dplyr, testthat, knitr,
R.rsp, MCMCpack

Bayesian Methods for Image Segmentation using a Potts Model
c(
person("Matt", "Moores", role = c("aut", "cre"), email = "M.T.Moores@warwick.ac.uk"),
person("Kerrie", "Mengersen", role="aut", email="k.mengersen@qut.edu.au"))
Various algorithms for segmentation of 2D and 3D images, such
as computed tomography and satellite remote sensing. This package implements
Bayesian image analysis using the hidden Potts model with external field
prior. Latent labels are sampled using chequerboard updating or Swendsen-Wang.
Algorithms for the smoothing parameter include pseudolikelihood, path sampling,
the exchange algorithm, and approximate Bayesian computation (ABC).
Matt Moores [aut, cre],
Kerrie Mengersen [aut]

c(
person("Matt", "Moores", role = c("aut", "cre"), email = "M.T.Moores@warwick.ac.uk"),
person("Kerrie", "Mengersen", role="aut", email="k.mengersen@qut.edu.au"),
person("Dai", "Feng", role="ctb"))
Matt Moores [aut, cre],
Kerrie Mengersen [aut],
Dai Feng [ctb]

c(
person("Matt", "Moores", role = c("aut", "cre"), email = "M.T.Moores@warwick.ac.uk", comment = c(ORCID = "0000-0003-4531-3572")),
person("Kerrie", "Mengersen", role=c("aut", "ths"), email="k.mengersen@qut.edu.au", comment = c(ORCID = "0000-0001-8625-9168")),
person("Dai", "Feng", role="ctb"))
Various algorithms for segmentation of 2D and 3D images, such
as computed tomography and satellite remote sensing. This package implements
Bayesian image analysis using the hidden Potts model with external field
prior. Latent labels are sampled using chequerboard updating or Swendsen-Wang.
Algorithms for the smoothing parameter include pseudolikelihood, path sampling,
the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC),
and Bayesian indirect likelihood (BIL).