Package: RBF 2.1.1

RBF: Robust Backfitting

A robust backfitting algorithm for additive models based on (robust) local polynomial kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators, and it computes predictions for points outside the training set if desired. See Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077> and Martinez and Salibian-Barrera (2021) <doi:10.21105/joss.02992> for details.

Authors:Matias Salibian-Barrera [aut, cre], Alejandra Martinez [aut]

RBF_2.1.1.tar.gz
RBF_2.1.1.zip(r-4.5)RBF_2.1.1.zip(r-4.4)RBF_2.1.1.zip(r-4.3)
RBF_2.1.1.tgz(r-4.5-x86_64)RBF_2.1.1.tgz(r-4.5-arm64)RBF_2.1.1.tgz(r-4.4-x86_64)RBF_2.1.1.tgz(r-4.4-arm64)RBF_2.1.1.tgz(r-4.3-x86_64)RBF_2.1.1.tgz(r-4.3-arm64)
RBF_2.1.1.tar.gz(r-4.5-noble)RBF_2.1.1.tar.gz(r-4.4-noble)
RBF_2.1.1.tgz(r-4.4-emscripten)RBF_2.1.1.tgz(r-4.3-emscripten)
RBF.pdf |RBF.html✨
RBF/json (API)
NEWS

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

Bug tracker:https://github.com/msalibian/rbf/issues

On CRAN:

Conda-Forge:

5.11 score 2 stars 13 scripts 200 downloads 41 mentions 5 exports 0 dependencies

Last updated 2 years agofrom:3b0d553995. Checks:12 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 06 2025
R-4.5-win-x86_64OKMar 06 2025
R-4.5-mac-x86_64OKMar 06 2025
R-4.5-mac-aarch64OKMar 06 2025
R-4.5-linux-x86_64OKMar 06 2025
R-4.4-win-x86_64OKMar 06 2025
R-4.4-mac-x86_64OKMar 06 2025
R-4.4-mac-aarch64OKMar 06 2025
R-4.4-linux-x86_64OKMar 06 2025
R-4.3-win-x86_64OKMar 06 2025
R-4.3-mac-x86_64OKMar 06 2025
R-4.3-mac-aarch64OKMar 06 2025

Exports:backf.clbackf.robk.epanpsi.huberpsi.tukey

Dependencies:

Examples

Martínez and Salibian

Rendered fromExamples.Rmdusingknitr::rmarkdownon Mar 06 2025.

Last update: 2021-04-08
Started: 2021-02-12