Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning R package spBPS version 2.0-1 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, 2025) <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. Software Depends: R (>= 1.8.0) Imports: Rcpp, CVXR (>= 1.8.1), mniw LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, abind, mvnfast, ECOSolveR, foreach, parallel, doParallel, tictoc, MBA, RColorBrewer, classInt, sp, fields, testthat (>= 3.0.0) Luca Presicce <l.presicce@campus.unimib.it> Comprehensive R Archive Network (CRAN) Luca Presicce [aut, cre] (ORCID: <https://orcid.org/0009-0005-7062-3523>), Sudipto Banerjee [aut] GPL (>= 3) 2026-05-14 application/tgz https://CRAN.R-project.org/package=spBPS doi:10.32614/CRAN.package.spBPS