Package: RSTr Type: Package Title: Gibbs Samplers for Discrete Bayesian Spatiotemporal Models Version: 1.2.0 Authors@R: c(person(given = "David", family = "DeLara", role = c("aut", "cre"), email = "sfq1@cdc.gov", comment = c("https://orcid.org/0000-0003-0485-7549")), person("Centers for Disease Control and Prevention", role = c("aut", "cph"), comment = c("https://ror.org/042twtr12"))) Maintainer: David DeLara URL: https://cehicapacitybuilding.org/RSTr/ Description: Takes Poisson or Binomial discrete spatial data and runs a Gibbs sampler for a variety of Spatiotemporal Conditional Autoregressive (CAR) models. Includes measures to prevent estimate over-smoothing through a restriction of model informativeness for select models. Also provides tools to load output and get median estimates. Implements methods from Besag, York, and MolliƩ (1991) "Bayesian image restoration, with two applications in spatial statistics" , Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" , Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" , and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-MolliƩ CAR model" . License: GPL (>= 3) Encoding: UTF-8 LinkingTo: Rcpp (>= 1.1.0), RcppArmadillo (>= 15.2.2.1), RcppDist (>= 0.1.1.1) Suggests: ggplot2, knitr, rmarkdown, sf, testthat (>= 3.0.0) Imports: abind, matrixStats, spdep Depends: R (>= 4.3.0) LazyData: true RoxygenNote: 7.3.3 VignetteBuilder: knitr, rmarkdown Config/testthat/edition: 3 Config/pak/sysreqs: libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev libssl-dev libproj-dev libsqlite3-dev libudunits2-dev Repository: https://cehi-code-repos.r-universe.dev Date/Publication: 2026-07-08 13:57:44 UTC RemoteUrl: https://github.com/CEHI-code-repos/RSTr RemoteRef: HEAD RemoteSha: 8eaf073135f1fb5a3b44d6fc8ff873d9fc3ab075 NeedsCompilation: yes Packaged: 2026-10-06 10:52:31 UTC; root Author: David DeLara [aut, cre] (ORCID: ), Centers for Disease Control and Prevention [aut, cph] (https://ror.org/042twtr12)