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:	1.5-4
Depends:	R (≥ 4.4.0)
Imports:	terra, randomForest, data.table, pbapply, qmap, hydroGOF
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), covr
Published:	2025-04-01
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.