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. Four
estimators for the kernel width hyper-parameter are provided (S1: Caputo,
S2: pairwise median, S3: residual variance, S4: corrected Akaike
bandwidth), together with a data-driven selection rule. Inference is
provided by a sandwich variance estimator with a family of
heteroskedasticity-consistent finite-sample corrections adapted to the
weighted leverage of the method, or by a pairs bootstrap that re-estimates
the kernel width on each replicate (percentile, normal and bias-corrected
accelerated intervals with p-values). Six real datasets from the robust
regression literature are included to facilitate reproducible comparisons.
| Version: |
0.5.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
stats, graphics, grDevices, MASS, sm |
| Suggests: |
robustbase, quantreg, testthat (≥ 3.0.0), knitr, rmarkdown |
| Published: |
2026-10-14 |
| 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:
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