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CRAN: Package RFplus

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 ORCID iD [aut, cre, cph], Alex Avilés ORCID iD [aut], Darío Zhiña ORCID iD [aut], Marco Mogro ORCID iD [aut], Anthony Guamán 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.html , RFplus.pdf
Vignettes: Machine learning algorithm for fusing ground and satellite precipitation data (source, R code)

Downloads:

Package source: RFplus_1.5-4.tar.gz
Windows binaries: r-devel: RFplus_1.5-4.zip, r-release: RFplus_1.5-4.zip, r-oldrel: RFplus_1.5-4.zip
macOS binaries: r-release (arm64): RFplus_1.5-4.tgz, r-oldrel (arm64): RFplus_1.5-4.tgz, r-release (x86_64): RFplus_1.5-4.tgz, r-oldrel (x86_64): RFplus_1.5-4.tgz
Old sources: RFplus archive

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