gkrreg
0.4.0Gaussian Kernel Robust Regression (GKRReg)
Overview
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.
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- OK2026-06-187 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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4.4 avg / 16 max
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Package metadata
- First published
- 2026-06-17
- Total releases
- 1 / 1 yrs
- License
- GPL-3 OSI
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