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:

Reference manual:	spStack.pdf
Vignettes:	spStack: Bayesian Geostatistics Using Predictive Stacking (source, R code)

Downloads:

Package source:	spStack_1.0.0.tar.gz
Windows binaries:	r-devel: spStack_1.0.0.zip, r-release: spStack_1.0.0.zip, r-oldrel: spStack_1.0.0.zip
macOS binaries:	r-release (arm64): spStack_1.0.0.tgz, r-oldrel (arm64): spStack_1.0.0.tgz, r-release (x86_64): spStack_1.0.0.tgz, r-oldrel (x86_64): spStack_1.0.0.tgz

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