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.

Version: 2.1.0
Imports: stats, graphics
Suggests: knitr, rmarkdown, gam, RobStatTM, MASS
Published: 2021-04-09
Author: Matias Salibian-Barrera [aut, cre], Alejandra Martinez [aut]
Maintainer: Matias Salibian-Barrera <matias at stat.ubc.ca>
License: GPL (≥ 3.0)
NeedsCompilation: yes
Materials: NEWS
CRAN checks: RBF results

Downloads:

Reference manual: RBF.pdf
Vignettes: Examples
Package source: RBF_2.1.0.tar.gz
Windows binaries: r-devel: RBF_2.1.0.zip, r-release: RBF_2.1.0.zip, r-oldrel: RBF_2.1.0.zip
macOS binaries: r-release: RBF_2.1.0.tgz, r-oldrel: RBF_2.1.0.tgz
Old sources: RBF archive

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