doi:10.48550/arXiv.2304.12414>, and, Pan, Zhang, Bradley, and Banerjee (2024) <doi:10.48550/arXiv.2406.04655> for details.">

spStack: Bayesian Geostatistics Using Predictive Stacking (original) (raw)

Fits Bayesian hierarchical spatial 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 some 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 (2024) <doi:10.48550/arXiv.2304.12414>, and, Pan, Zhang, Bradley, and Banerjee (2024) <doi:10.48550/arXiv.2406.04655> for details.

Version: 1.0.1
Depends: R (≥ 2.10)
Imports: CVXR, future, future.apply, ggplot2, MBA, rstudioapi
Suggests: ggpubr, knitr, rmarkdown, spelling, testthat (≥ 3.0.0)
Published: 2024-10-08
DOI: 10.32614/CRAN.package.spStack
Author: Soumyakanti Pan ORCID iD [aut, cre], Sudipto Banerjee [aut]
Maintainer: Soumyakanti Pan
BugReports: https://github.com/SPan-18/spStack-dev/issues
License: GPL-3
URL: https://github.com/SPan-18/spStack-dev,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 formhttps://CRAN.R-project.org/package=spStackto link to this page.