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RSTr: Gibbs Samplers for Discrete Bayesian Spatiotemporal Models
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 Mollié (1991) "Bayesian image restoration, with two applications in spatial statistics" <doi:10.1007/BF00116466>, Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" <doi:10.1093/biostatistics/4.1.11>, Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" <doi:10.1214/17-AOAS1068>, and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-Mollié CAR model" <doi:10.1016/j.sste.2021.100420>.
Getting started
Package overview
01: Understanding and Preparing Your Event Data
02: Understanding and Preparing Your Adjacency Structure
03: The CAR Models
04: Generating Estimates: Age-standardization
05: Generating Estimates: Reliability and Suppression
06: Model Informativeness
07: Sample Processing
08: Initial Values
09: Priors
An Introduction to the RSTr Package
Appendix A: The CAR Hierarchical Models
Appendix B: Troubleshooting
Browse package contents
Package details |
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| Author | David DeLara [aut, cre] (ORCID: <https://orcid.org/0000-0003-0485-7549>), Centers for Disease Control and Prevention [aut, cph] (https://ror.org/042twtr12) |
| Maintainer | David DeLara <sfq1@cdc.gov> |
| License | GPL (>= 3) |
| Version | 1.1.4 |
| URL | https://cehi-code-repos.github.io/RSTr/ |
| Package repository | View on CRAN |
| Installation |
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