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ref.ICAR: Objective Bayes Intrinsic Conditional Autoregressive Model for Areal Data

Implements an objective Bayes intrinsic conditional autoregressive prior. This model provides an objective Bayesian approach for modeling spatially correlated areal data using an intrinsic conditional autoregressive prior on a vector of spatial random effects.

Version: 2.0.1
Imports: sf, sp, spdep, mvtnorm, coda, MCMCglmm, Rdpack, graphics, pracma, stats, classInt, dplyr, ggplot2, gtools
Suggests: maps, MASS, knitr, rmarkdown, RColorBrewer, rcrossref
Published: 2023-08-22
DOI: 10.32614/CRAN.package.ref.ICAR
Author: Erica M. Porter, Matthew J. Keefe, Christopher T. Franck, and Marco A.R. Ferreira
Maintainer: Erica M. Porter <emporte at clemson.edu>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: ref.ICAR results

Documentation:

Reference manual: ref.ICAR.pdf
Vignettes: Applying an ICAR reference prior

Downloads:

Package source: ref.ICAR_2.0.1.tar.gz
Windows binaries: r-devel: ref.ICAR_2.0.1.zip, r-release: ref.ICAR_2.0.1.zip, r-oldrel: ref.ICAR_2.0.1.zip
macOS binaries: r-release (arm64): ref.ICAR_2.0.1.tgz, r-oldrel (arm64): ref.ICAR_2.0.1.tgz, r-release (x86_64): ref.ICAR_2.0.1.tgz, r-oldrel (x86_64): ref.ICAR_2.0.1.tgz
Old sources: ref.ICAR archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.