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)
    <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.
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: 2026-06-07 13:43:30 UTC; marceloferreira
Author: Eufrásio de Andrade Lima Neto [aut],
  Marcelo Rodrigo Portela Ferreira [aut, cre]
Maintainer: Marcelo Rodrigo Portela Ferreira <marcelo@de.ufpb.br>
Repository: CRAN
Date/Publication: 2026-06-17 13:40:02 UTC
