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