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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: | 2.1.1 |
| Imports: | stats, graphics |
| Suggests: | knitr, rmarkdown, gam, RobStatTM, MASS |
| Published: | 2023-08-31 |
| 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 |
| Reference manual: | RBF.pdf |
| Vignettes: |
Examples |
| Package source: | RBF_2.1.1.tar.gz |
| Windows binaries: | r-devel: RBF_2.1.1.zip, r-release: RBF_2.1.1.zip, r-oldrel: RBF_2.1.1.zip |
| macOS binaries: | r-release (arm64): RBF_2.1.1.tgz, r-oldrel (arm64): RBF_2.1.1.tgz, r-release (x86_64): RBF_2.1.1.tgz, r-oldrel (x86_64): RBF_2.1.1.tgz |
| Old sources: | RBF archive |
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