The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
RFplus: Machine Learning for Merging Satellite and Ground Precipitation
Data
A machine learning algorithm that merges satellite and ground precipitation data using Random Forest for spatial prediction, residual modeling for bias correction, and quantile mapping for adjustment, ensuring accurate estimates across temporal scales and regions.
| Version: |
1.4-0 |
| Depends: |
R (≥ 4.4.0) |
| Imports: |
terra, randomForest, data.table, pbapply, qmap, hydroGOF |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0), covr |
| Published: |
2025-03-10 |
| DOI: |
10.32614/CRAN.package.RFplus |
| Author: |
Jonnathan Augusto Landi Bermeo
 [aut, cre,
cph],
Alex Avilés [aut],
Darío Zhiña [aut],
Marco Mogro [aut],
Anthony Guamán
[aut] |
| Maintainer: |
Jonnathan Augusto Landi Bermeo <jonnathan.landi at outlook.com> |
| BugReports: |
https://github.com/Jonnathan-Landi/RFplus/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/Jonnathan-Landi/RFplus |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
RFplus results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=RFplus
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.