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gkrreg  

Gaussian Kernel Robust Regression (GKRReg)
View on CRAN: Click here


Download and install gkrreg package within the R console
Install from CRAN:
install.packages("gkrreg")

Install from Github:
library("remotes")
install_github("cran/gkrreg")

Install by package version:
library("remotes")
install_version("gkrreg", "0.4.0")



Attach the package and use:
library("gkrreg")
Maintained by
Marcelo Rodrigo Portela Ferreira
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-17
Latest Update: 2026-06-17
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.
How to cite:
Marcelo Rodrigo Portela Ferreira (2026). gkrreg: Gaussian Kernel Robust Regression (GKRReg). R package version 0.4.0, https://cran.r-project.org/web/packages/gkrreg. Accessed 09 Oct. 2026.
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