Title: Scalable Rejection Sampling for Bayesian Hierarchical Models
Authors@R: person(family="Braun", given="Michael", role=c("aut","cre","cph"), email="braunm@smu.edu")
Description: 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.
Suggests: sparseHessianFD(>= 0.2.0), sparseMVN(>= 0.2.0), mvtnorm,
trustOptim (>= 0.8.5), plyr (>= 1.8), dplyr, testthat, knitr,

Description: 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
Suggests: sparseHessianFD(>= 0.3.0), sparseMVN(>= 0.2.0), mvtnorm,

Title: Bayesian Methods for Image Segmentation using a Potts Model
person("Matt", "Moores", role = c("aut", "cre"), email = "M.T.Moores@warwick.ac.uk"),
Description: 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).

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")),
the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC),

Latent labels are sampled using chequerboard updating or Swendsen-Wang.
and Bayesian indirect likelihood (BIL). Refer to <doi:10.1007/s11222-014-9525-6>
and <arXiv:1503.08066> for further details.
Author: Matt Moores [aut, cre] (<https://orcid.org/0000-0003-4531-3572>),
Kerrie Mengersen [aut, ths] (<https://orcid.org/0000-0001-8625-9168>),