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: 1.3-0
Depends: R (≥ 4.4.0)
Imports: terra, randomForest, data.table, pbapply, qmap, hydroGOF
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2025-02-24
DOI: 10.32614/CRAN.package.RFplus
Author: Jonnathan Augusto Landi Bermeo ORCID iD [aut, cre, cph], Alex Avilés ORCID iD [aut], Darío Zhiña ORCID iD [aut], Marco Mogro ORCID iD [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:

Reference manual: RFplus.pdf
Vignettes: Progressive Bias Correction of Satellite Environmental Data using RFplus (source, R code)

Downloads:

Package source: RFplus_1.3-0.tar.gz
Windows binaries: r-devel: RFplus_1.3-0.zip, r-release: RFplus_1.3-0.zip, r-oldrel: not available
macOS binaries: r-devel (arm64): RFplus_1.3-0.tgz, r-release (arm64): RFplus_1.3-0.tgz, r-oldrel (arm64): not available, r-devel (x86_64): RFplus_1.3-0.tgz, r-release (x86_64): RFplus_1.3-0.tgz, r-oldrel (x86_64): not available
Old sources: RFplus archive

Linking:

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