# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "spBPS" in publications use:' type: software license: GPL-3.0-or-later title: 'spBPS: Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning' version: 0.0-4 doi: 10.32614/CRAN.package.spBPS abstract: '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) . 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.' authors: - family-names: Presicce given-names: Luca email: l.presicce@campus.unimib.it orcid: https://orcid.org/0009-0005-7062-3523 - family-names: Banerjee given-names: Sudipto repository: https://lucapresicce.r-universe.dev commit: 389cd729ed738f8f559cdd75030250c18e74bc4b contact: - family-names: Presicce given-names: Luca email: l.presicce@campus.unimib.it orcid: https://orcid.org/0009-0005-7062-3523