Package: RFplus 1.4-0

Jonnathan Augusto Landi Bermeo

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.

Authors:Jonnathan Augusto Landi Bermeo [aut, cre, cph], Alex Avilés [aut], Darío Zhiña [aut], Marco Mogro [aut], Anthony Guamán [aut]

RFplus_1.4-0.tar.gz
RFplus_1.4-0.tar.gz(r-4.5-noble)RFplus_1.4-0.tar.gz(r-4.4-noble)
RFplus_1.4-0.tgz(r-4.4-emscripten)
RFplus.pdf |RFplus.html✨
RFplus/json (API)
NEWS

# Install 'RFplus' in R:
install.packages('RFplus', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jonnathan-landi/rfplus/issues

Datasets:
  • BD_Insitu - Precipitation Station Measurement Dataset
  • Cords_Insitu - Precipitation Station Coordinates Dataset

On CRAN:

Conda:

3.18 score 205 downloads 1 exports 21 dependencies

Last updated 4 days agofrom:c3644f5d64. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 10 2025
R-4.5-linuxOKMar 10 2025
R-4.4-linuxOKMar 10 2025

Exports:RFplus

Dependencies:classclassIntdata.tablee1071fitdistrplushydroGOFhydroTSMKernSmoothlatticeMASSMatrixpbapplyproxyqmaprandomForestRcpprlangsurvivalterraxtszoo

RFplus: A Novel Machine Learning Approach for Merging Multi-Satellite Precipitation Products and Ground Observations

Jonnathan Augusto Landi Bermeo

Rendered fromRFplus.Rmdusingknitr::rmarkdownon Mar 10 2025.

Last update: 2025-03-10
Started: 2025-02-04