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

Documentation:

Reference manual:	RBF.pdf
Vignettes:	Examples

Downloads:

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 (arm64): RBF_2.1.0.tgz, r-oldrel (arm64): RBF_2.1.0.tgz, r-release (x86_64): RBF_2.1.0.tgz, r-oldrel (x86_64): RBF_2.1.0.tgz
Old sources:	RBF archive

Linking:

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