Package: spBPS Title: Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning Version: 2.0-1 Authors@R: c( person("Luca", "Presicce", role = c("aut", "cre"), email = "l.presicce@campus.unimib.it", comment = c(ORCID = "0009-0005-7062-3523")), person("Sudipto", "Banerjee", role = "aut")) Maintainer: Luca Presicce Author: Luca Presicce [aut, cre] (), Sudipto Banerjee [aut] Description: 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) . 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. 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) Config/testthat/edition: 3 License: GPL (>= 3) Encoding: UTF-8 Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.3 VignetteBuilder: knitr URL: https://lucapresicce.github.io/spBPS/ Config/pak/sysreqs: cmake libgmp3-dev make pkg-config libclang-dev Repository: https://lucapresicce.r-universe.dev Date/Publication: 2026-05-07 16:24:47 UTC RemoteUrl: https://github.com/lucapresicce/spbps RemoteRef: HEAD RemoteSha: 7881eacb2894b4e203386e7e13a584429683d5a1 NeedsCompilation: yes Packaged: 2026-06-24 13:14:51 UTC; root