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This package provides the principal functions to perform accelerated modeling for univariate and multivariate spatial regressions. The package is used mostly within the novel working paper “Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach” (Luca Presicce and Sudipto Banerjee, 2024+)“. To guarantee the reproducibility of scientific results, in the Bayesian-Transfer-Learning-for-GeoAI repository are also available all the scripts of code used for simulations, data analysis, and results presented in the Manuscript and its Supplemental material.
| ## Roadmap |
|---|
## Guided installation Since the package
is not already available on CRAN (already submitted, and hopefully soon
available), we use the devtools R package to install. Then,
check for its presence on your device, otherwise install it:
{r, echo = F, eval = F, collapse = TRUE} if (!require(devtools)) { install.packages("devtools", dependencies = TRUE) }
Once you have installed devtools, we can proceed. Let’s install
the spBPS package! |
| Author | Luca Presicce (l.presicce@campus.unimib.it) & Sudipto Banerjee (sudipto@ucla.edu) |
| Maintainer | Luca Presicce (l.presicce@campus.unimib.it) |
| Reference | Luca Presicce and Sudipto Banerjee (2024+) “Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach” |
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.