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RBF  

Robust Backfitting
View on CRAN: Click here


Download and install RBF package within the R console
Install from CRAN:
install.packages("RBF")

Install from Github:
library("remotes")
install_github("cran/RBF")

Install by package version:
library("remotes")
install_version("RBF", "2.1.1")



Attach the package and use:
library("RBF")
Maintained by
Matias Salibian-Barrera
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-06-14
Latest Update: 2023-08-31
Description:
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) and Martinez and Salibian-Barrera (2021) for details.
How to cite:
Matias Salibian-Barrera (2019). RBF: Robust Backfitting. R package version 2.1.1, https://cran.r-project.org/web/packages/RBF. Accessed 21 Nov. 2024.
Previous versions and publish date:
1.0.0 (2019-06-14 10:20), 2.0.1 (2020-07-30 06:40), 2.1.0 (2021-04-10 01:40)
Other packages that cited RBF R package
View RBF citation profile
Other R packages that RBF depends, imports, suggests or enhances
Complete documentation for RBF
Functions, R codes and Examples using the RBF R package
Some associated functions: RBF-package . backf.cl . backf.rob . deviance.backf . fitted.values.backf . formula.backf . k.epan . plot.backf . predict.backf . print.backf . psi.huber . psi.tukey . residuals.backf . summary.backf . 
Some associated R codes: RBF-fn.R .  Full RBF package functions and examples
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