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spBPS  

Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning
View on CRAN: Click here


Download and install spBPS package within the R console
Install from CRAN:
install.packages("spBPS")

Install from Github:
library("remotes")
install_github("cran/spBPS")

Install by package version:
library("remotes")
install_version("spBPS", "0.0-4")



Attach the package and use:
library("spBPS")
Maintained by
Luca Presicce
[Scholar Profile | Author Map]
First Published: 2024-10-25
Latest Update: 2024-10-25
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, 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.
How to cite:
Luca Presicce (2024). spBPS: Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning. R package version 0.0-4, https://cran.r-project.org/web/packages/spBPS. Accessed 10 Apr. 2025.
Previous versions and publish date:
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Complete documentation for spBPS
Functions, R codes and Examples using the spBPS R package
Full spBPS package functions and examples
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