spBPS: Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning

Provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2024) <doi:10.48550/arXiv.2410.09504>. This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios.

Version:	0.0-4
Depends:	R (≥ 1.8.0)
Imports:	Rcpp, CVXR, mniw
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	knitr, rmarkdown, mvnfast, foreach, parallel, doParallel, tictoc, MBA, RColorBrewer, classInt, sp, fields, testthat (≥ 3.0.0)
Published:	2024-10-25
DOI:	10.32614/CRAN.package.spBPS
Author:	Luca Presicce [aut, cre], Sudipto Banerjee [aut]
Maintainer:	Luca Presicce <l.presicce at campus.unimib.it>
License:	GPL (≥ 3)
NeedsCompilation:	yes
Materials:	README
CRAN checks:	spBPS results

Documentation:

Reference manual:	spBPS.pdf
Vignettes:	Double Bayesian Predictive Stacking for (univariate) Spatial Analysis - Tutotial (source, R code)

Downloads:

Package source:	spBPS_0.0-4.tar.gz
Windows binaries:	r-devel: spBPS_0.0-4.zip, r-release: spBPS_0.0-4.zip, r-oldrel: spBPS_0.0-4.zip
macOS binaries:	r-release (arm64): spBPS_0.0-4.tgz, r-oldrel (arm64): spBPS_0.0-4.tgz, r-release (x86_64): spBPS_0.0-4.tgz, r-oldrel (x86_64): spBPS_0.0-4.tgz

Linking:

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