Package: RSTr 1.1.4

David DeLara

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>.

Authors:David DeLara [aut, cre], Centers for Disease Control and Prevention [aut, cph]

RSTr_1.1.4.tar.gz
RSTr_1.1.4.tar.gz(r-4.7-arm64)RSTr_1.1.4.tar.gz(r-4.7-x86_64)RSTr_1.1.4.tar.gz(r-4.6-arm64)RSTr_1.1.4.tar.gz(r-4.6-x86_64)
RSTr_1.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
RSTr/json (API)

# Install 'RSTr' in R:
install.packages('RSTr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cehi-code-repos/rstr/issues

Pkgdown/docs site:https://cehi-code-repos.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • maexample - Massachusetts Heart Attack Mortality Data
  • mamap - Massachusetts Shapefile
  • miadj - Michigan Adjacency Data
  • miheart - Michigan Heart Attack Mortality Data
  • minsample - Samples Generated for Michigan data
  • minsplit - Age- and Sex-stratified Samples for Michigan data
  • mishp - Michigan Shapefile

On CRAN:

Conda:

openblascppopenmp

3.76 score 24 scripts 148 downloads 19 exports 22 dependencies

Last updated from:8abc989ad4. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK230
linux-devel-x86_64OK215
source / vignettesOK321
linux-release-arm64OK208
linux-release-x86_64OK207
wasm-releaseOK178

Exports:add_neighborsage_standardizeaggregate_countaggregate_samplescarget_credible_intervalget_estimatesget_mediansget_relative_precisionload_modelload_sampleslong_to_list_matrixmcarmstcarrcarsplit_sample_groupsstandardize_samplessuppress_estimatesupdate_model

Dependencies:abindbootclassclassIntDBIdeldire1071KernSmoothlatticeMASSmatrixStatsproxyRcppRcppArmadilloRcppDists2sfspspDataspdepunitswk

01: Understanding and Preparing Your Event Data

Rendered fromRSTr-event.Rmdusingknitr::rmarkdownon May 31 2026.

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02: Understanding and Preparing Your Adjacency Structure

Rendered fromRSTr-adjacency.Rmdusingknitr::rmarkdownon May 31 2026.

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03: The CAR Models

Rendered fromRSTr-car.Rmdusingknitr::rmarkdownon May 31 2026.

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04: Generating Estimates: Age-standardization

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05: Generating Estimates: Reliability and Suppression

Rendered fromRSTr-reliability.Rmdusingknitr::rmarkdownon May 31 2026.

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06: Model Informativeness

Rendered fromRSTr-informativeness.Rmdusingknitr::rmarkdownon May 31 2026.

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07: Sample Processing

Rendered fromRSTr-samples.Rmdusingknitr::rmarkdownon May 31 2026.

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08: Initial Values

Rendered fromRSTr-initialvalues.Rmdusingknitr::rmarkdownon May 31 2026.

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09: Priors

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An Introduction to the RSTr Package

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Appendix A: The CAR Hierarchical Models

Rendered fromRSTr-models.Rmdusingknitr::rmarkdownon May 31 2026.

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Appendix B: Troubleshooting

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