bayesImageS: Bayesian Methods for Image Segmentation using a Potts Model

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 of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>. 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 the parametric functional approximate Bayesian (PFAB) algorithm. Refer to <doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for further details.

Version:	0.6-0
Depends:	R (≥ 3.0.0)
Imports:	Rcpp (≥ 0.10.6)
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	mcmcse, coda, PottsUtils, rstan, knitr, rmarkdown, lattice
Published:	2019-01-04
Author:	Matt Moores [aut, cre], Kerrie Mengersen [aut, ths], Dai Feng [ctb]
Maintainer:	Matt Moores <mmoores at gmail.com>
BugReports:	https://bitbucket.org/Azeari/bayesimages/issues
License:	GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
URL:	https://bitbucket.org/Azeari/bayesimages, https://mooresm.github.io/bayesImageS/
NeedsCompilation:	yes
Citation:	bayesImageS citation info
Materials:	README NEWS
In views:	Bayesian, MedicalImaging
CRAN checks:	bayesImageS results

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