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], DarÃo 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.
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