Gaussian Kernel Robust Regression (GKRReg) R package gkrreg version 0.4.0 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. Software Depends: R (>= 4.0.0) Imports: stats, graphics, grDevices, MASS, sm Suggests: robustbase, quantreg, testthat (>= 3.0.0), knitr, rmarkdown Marcelo Rodrigo Portela Ferreira <marcelo@de.ufpb.br> Comprehensive R Archive Network (CRAN) Eufrásio de Andrade Lima Neto [aut], Marcelo Rodrigo Portela Ferreira [aut, cre] GPL-3 2026-06-17 application/tgz https://CRAN.R-project.org/package=gkrreg doi:10.32614/CRAN.package.gkrreg