{ "_id": "6a1bec9f1d7bb097a0a1fdb6", "Package": "RSTr", "Type": "Package", "Title": "Gibbs Samplers for Discrete Bayesian Spatiotemporal Models", "Version": "1.1.4", "Authors@R": "c(person(given = \"David\",\nfamily = \"DeLara\",\nrole = c(\"aut\", \"cre\"),\nemail = \"sfq1@cdc.gov\",\ncomment = c(\"https://orcid.org/0000-0003-0485-7549\")),\nperson(\"Centers for Disease Control and Prevention\",\nrole = c(\"aut\", \"cph\"),\ncomment = c(\"https://ror.org/042twtr12\")))", "Maintainer": "David DeLara ", "URL": "https://cehi-code-repos.github.io/RSTr/", "Description": "Takes Poisson or Binomial discrete spatial data and runs a\nGibbs sampler for a variety of Spatiotemporal Conditional\nAutoregressive (CAR) models. Includes measures to prevent\nestimate over-smoothing through a restriction of model\ninformativeness for select models. Also provides tools to load\noutput and get median estimates. Implements methods from Besag,\nYork, and Mollié (1991) \"Bayesian image restoration, with two\napplications in spatial statistics\" ,\nGelfand and Vounatsou (2003) \"Proper multivariate conditional\nautoregressive models for spatial data analysis\"\n, Quick et al. (2017)\n\"Multivariate spatiotemporal modeling of age-specific stroke\nmortality\" , and Quick et al. 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