gkrreg: Gaussian Kernel Robust Regression (GKRReg)

Implements the Gaussian Kernel Robust Regression (GKRReg / GKRR) method proposed by De Carvalho, Lima Neto and Ferreira (2017) <doi:10.1016/j.neucom.2016.12.035>. The method re-weights observations iteratively using the Gaussian kernel so that poorly-fitted observations (outliers, leverage points) receive small weights, yielding resistance to Y-space outliers, X-space outliers and leverage points. Convergence is guaranteed by Propositions 4.1 and 4.2 of the original paper. Three estimators for the kernel width hyper-parameter are provided (S1: Caputo, S2: pairwise median, S3: residual variance). Inference is provided via an analytic sandwich variance estimator (default) or via bootstrap (percentile, normal and BCa intervals with p-values) through gkrr_boot(). Six real datasets from the robust regression literature are included to facilitate reproducible comparisons.

Version:	0.4.0
Depends:	R (≥ 4.0.0)
Imports:	stats, graphics, grDevices, MASS, sm
Suggests:	robustbase, quantreg, testthat (≥ 3.0.0), knitr, rmarkdown
Published:	2026-06-17
DOI:	10.32614/CRAN.package.gkrreg
Author:	Eufrásio de Andrade Lima Neto [aut], Marcelo Rodrigo Portela Ferreira [aut, cre]
Maintainer:	Marcelo Rodrigo Portela Ferreira <marcelo at de.ufpb.br>
BugReports:	https://github.com/marcelorpf/gkrreg/issues
License:	GPL-3
URL:	https://github.com/marcelorpf/gkrreg
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	gkrreg results

Documentation:

Reference manual:	gkrreg.html , gkrreg.pdf
Vignettes:	Introduction to gkrreg: Gaussian Kernel Robust Regression (source, R code)

Downloads:

Package source:	gkrreg_0.4.0.tar.gz
Windows binaries:	r-devel: gkrreg_0.4.0.zip, r-release: gkrreg_0.4.0.zip, r-oldrel: gkrreg_0.4.0.zip
macOS binaries:	r-release (arm64): gkrreg_0.4.0.tgz, r-oldrel (arm64): gkrreg_0.4.0.tgz, r-release (x86_64): gkrreg_0.4.0.tgz, r-oldrel (x86_64): gkrreg_0.4.0.tgz

Linking:

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