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.
- Version1.1.4
- R version≥ 4.3.0
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Last releasetoday at 12:00 AM
Documentation
- Vignette02: Understanding and Preparing Your Adjacency Structure
- Vignette04: Generating Estimates: Age-standardization
- Vignette03: The CAR Models
- Vignette01: Understanding and Preparing Your Event Data
- Vignette06: Model Informativeness
- Vignette08: Initial Values
- VignetteAppendix A: The CAR Hierarchical Models
- Vignette09: Priors
- Vignette05: Generating Estimates: Reliability and Suppression
- Vignette07: Sample Processing
- VignetteAppendix B: Troubleshooting
- VignetteAn Introduction to the RSTr Package
Team
David DeLara
MaintainerShow author detailsCenters for Disease Control and Prevention
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