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message: 'To cite package "gkrreg" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'gkrreg: Gaussian Kernel Robust Regression (GKRReg)'
version: 0.4.0
abstract: 'Implements the Gaussian Kernel Robust Regression (GKRReg / GKRR) method
  proposed by De Carvalho, Lima Neto and Ferreira (2017) <https://doi.org/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.'
authors:
- family-names: Andrade Lima Neto
  given-names: Eufrásio
  name-particle: de
  email: eufrasio@de.ufpb.br
- family-names: Rodrigo Portela Ferreira
  given-names: Marcelo
  email: marcelo@de.ufpb.br
repository: https://marcelorpf.r-universe.dev
repository-code: https://github.com/marcelorpf/gkrreg
commit: cc3e9917936e893567bf74a1dbeddae915dc66f1
url: https://github.com/marcelorpf/gkrreg
date-released: '2026-06-17'
contact:
- family-names: Rodrigo Portela Ferreira
  given-names: Marcelo
  email: marcelo@de.ufpb.br