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. 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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.4.0 |
rolling linux/jammy R-4.5 | gkrreg_0.4.0.tar.gz |
353.0 KiB |
0.4.0 |
rolling linux/noble R-4.5 | gkrreg_0.4.0.tar.gz |
353.1 KiB |
0.4.0 |
rolling source/ R- | gkrreg_0.4.0.tar.gz |
277.1 KiB |
0.4.0 |
latest linux/jammy R-4.5 | gkrreg_0.4.0.tar.gz |
353.0 KiB |
0.4.0 |
latest linux/noble R-4.5 | gkrreg_0.4.0.tar.gz |
353.1 KiB |
0.4.0 |
latest source/ R- | gkrreg_0.4.0.tar.gz |
277.1 KiB |
0.4.0 |
2026-04-23 source/ R- | gkrreg_0.4.0.tar.gz |
0 B |