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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=spBPS to link to this page.
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