spStack: Bayesian Geostatistics Using Predictive Stacking
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: |
1.0.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
CVXR, future, future.apply, ggplot2, MBA, rstudioapi |
| Suggests: |
ggpubr, knitr, rmarkdown, spelling, testthat (≥ 3.0.0) |
| Published: |
2024-10-03 |
| DOI: |
10.32614/CRAN.package.spStack |
| 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://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:
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