Maintainer: Erica M. Porter emporte@clemson.edu
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 of ref.ICAR includes minor changes:
Defines figure captions in the vignette in each code chunk rather
than using the captioner package (removed from
CRAN).
Corrected some minor typos in the references.
Updated the email address for the maintainer (Erica Porter) due to changing academic institutions.
Porter, E.M., Franck, C.T., and Ferreira, M.A.R. (2023), “Objective Bayesian model selection for spatial hierarchical models with intrinsic conditional autoregressive priors,” Bayesian Analysis, International Society for Bayesian Analysis, 1, 1–27. https://doi.org/10.1214/23-BA1375.
Ferreira, M.A.R., Porter, E.M., and Franck, C.T. (2021), “Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects,” Computational Statistics and Data Analysis, 162, 107264. https://doi.org/10.1016/j.csda.2021.107264.
Keefe, M.J., Ferreira, M.A.R., and Franck, C.T. (2018), “On the formal specification of sum-zero constrained intrinsic conditional autoregressive models,” Spatial Statistics, Elsevier {BV}, 24, 54–65. https://doi.org/10.1016/j.spasta.2018.03.007.
Keefe, M.J., Ferreira, M.A.R., and Franck, C.T. (2019), “Objective Bayesian analysis for Gaussian hierarchical models with intrinsic conditional autoregressive priors,” Bayesian Analysis, International Society for Bayesian Analysis, 14, 181–209. https://doi.org/10.1214/18-BA1107.