Bayesian spatial modeling powered by Stan. geostan provides access to a variety of hierarchical spatial models using the R formula interface, supporting a complete spatial analysis workflow with a suite of spatial analysis tools. It is designed primarily for public health and social science research but is generally applicable to modeling areal data. Unique features of the package include its spatial measurement error model (for inference with small area estimates such as those from the American Community Survey), its fast proper conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models, and its eigenvector spatial filtering (ESF) models. The package also supports spatial regression with raster layers.
Carpenter, B., Gelman, A., Hoffman, M.D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., Riddell, A., 2017. Stan: A probabilistic programming language. Journal of statistical software 76. doi:10.18637/jss.v076.i01 .
Donegan, C., Y. Chun and A. E. Hughes (2020). Bayesian estimation of spatial filters with Moran’s Eigenvectors and hierarchical shrinkage priors. Spatial Statistics. doi:10.1016/j.spasta.2020.100450 (open access: doi:10.31219/osf.io/fah3z ).
Donegan, Connor and Chun, Yongwan and Griffith, Daniel A. (2021). Modeling community health with areal data: Bayesian inference with survey standard errors and spatial structure. Int. J. Env. Res. and Public Health 18 (13): 6856. doi:10.3390/ijerph18136856 . Supplementary material: https://github.com/ConnorDonegan/survey-HBM.
Donegan, Connor (2021). Building spatial conditional autoregressive models in the Stan programming language. OSF Preprints. doi:10.31219/osf.io/3ey65 .
Donegan, Connor (2022) geostan: An R package for Bayesian spatial analysis. The Journal of Open Source Software. 7, no. 79: 4716. doi:10.21105/joss.04716 .
Gabry, J., Goodrich, B. and Lysy, M. (2020). rstantools: Tools for developers of R packages interfacing with Stan. R package version 2.1.1 https://mc-stan.org/rstantools/.
Morris, M., Wheeler-Martin, K., Simpson, D., Mooney, S. J., Gelman, A., & DiMaggio, C. (2019). Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan. Spatial and spatio-temporal epidemiology, 31, 100301. doi:10.1016/j.sste.2019.100301 .
Stan Development Team (2019). RStan: the R interface to Stan. R package version 2.19.2. https://mc-stan.org