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spBPS: Bayesian Predictive Stacking for Scalable Geospatial Transfer
Learning
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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