Fits Bayesian hierarchical spatial and spatial-temporal process models for point-referenced Gaussian, Poisson, binomial, and binary data using stacking of predictive densities. It involves sampling from analytically available posterior distributions conditional upon candidate values of the spatial process parameters and, subsequently assimilate inference from these individual posterior distributions using Bayesian predictive stacking. Our algorithm is highly parallelizable and hence, much faster than traditional Markov chain Monte Carlo algorithms while delivering competitive predictive performance. See Zhang, Tang, and Banerjee (2025) <doi:10.48550/arXiv.2304.12414>, and, Pan, Zhang, Bradley, and Banerjee (2025) <doi:10.48550/arXiv.2406.04655> for details.
Version: 1.1.0 Depends: R (≥ 3.5) Imports: CVXR, future, future.apply, ggplot2, MBA, rstudioapi Suggests: dplyr, ggpubr, knitr, patchwork, rmarkdown, spelling, testthat (≥ 3.0.0), tidyr Published: 2025-07-12 Author: Soumyakanti Pan [aut, cre], Sudipto Banerjee [aut] Maintainer: Soumyakanti Pan <span18 at ucla.edu> BugReports: https://github.com/SPan-18/spStack-dev/issues License: GPL-3 URL: https://span-18.github.io/spStack-dev/ NeedsCompilation: yes Language: en-US Materials: README NEWS CRAN checks: spStack results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=spStack to link to this page.
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