Machine Learning for Merging Satellite and Ground Precipitation Data R package RFplus version 1.5-4 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. Software Depends: R (>= 4.4.0) Imports: terra, randomForest, data.table, pbapply, qmap, hydroGOF Suggests: knitr, rmarkdown, testthat (>= 3.0.0), covr Jonnathan Augusto Landi Bermeo <jonnathan.landi@outlook.com> Comprehensive R Archive Network (CRAN) Jonnathan Augusto Landi Bermeo [aut, cre, cph] (ORCID: <https://orcid.org/0009-0003-3162-6647>), Alex Avilés [aut] (ORCID: <https://orcid.org/0000-0001-9278-5738>), Darío Zhiña [aut] (ORCID: <https://orcid.org/0000-0001-9556-4025>), Marco Mogro [aut] (ORCID: <https://orcid.org/0009-0007-1802-9417>), Anthony Guamán [aut] (ORCID: <https://orcid.org/0009-0005-0204-7536>) GPL (>= 3) 2025-04-01 application/tgz https://CRAN.R-project.org/package=RFplus doi:10.32614/CRAN.package.RFplus