Package: gkrreg Type: Package Title: Gaussian Kernel Robust Regression (GKRReg) Version: 0.4.0 Authors@R: c( person("Eufrásio", "de Andrade Lima Neto", role = "aut", email = "eufrasio@de.ufpb.br"), person("Marcelo", "Rodrigo Portela Ferreira", role = c("aut", "cre"), email = "marcelo@de.ufpb.br") ) Description: Implements the Gaussian Kernel Robust Regression (GKRReg / GKRR) method proposed by De Carvalho, Lima Neto and Ferreira (2017) . 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. License: GPL-3 Encoding: UTF-8 LazyData: true LazyDataCompression: xz Depends: R (>= 4.0.0) Imports: stats, graphics, grDevices, MASS, sm Suggests: robustbase, quantreg, testthat (>= 3.0.0), knitr, rmarkdown Config/testthat/edition: 3 VignetteBuilder: knitr URL: https://github.com/marcelorpf/gkrreg BugReports: https://github.com/marcelorpf/gkrreg/issues Config/roxygen2/version: 8.0.0 NeedsCompilation: no Packaged: 2026-06-17 18:00:04 UTC; root Author: Eufrásio de Andrade Lima Neto [aut], Marcelo Rodrigo Portela Ferreira [aut, cre] Maintainer: Marcelo Rodrigo Portela Ferreira Repository: https://cran.r-universe.dev Date/Publication: 2026-06-17 13:40:02 UTC RemoteUrl: https://github.com/cran/gkrreg RemoteRef: HEAD RemoteSha: e8e5ba495c6df0e54c6bc11ab585d2403d5eb1f0