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 <l.presicce@campus.unimib.it>
Author: Luca Presicce [aut, cre] (ORCID:
        <https://orcid.org/0009-0005-7062-3523>), 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)
        <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.
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
RoxygenNote: 7.3.3
VignetteBuilder: knitr
URL: https://lucapresicce.github.io/spBPS/
NeedsCompilation: yes
Packaged: 2026-10-28 11:30:47 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2026-05-14 15:39:05 UTC
RemoteUrl: https://github.com/cran/spBPS
RemoteRef: HEAD
RemoteSha: 59dfb033b39a210c280e5f9053c862bfb12a0b47