https://marcelorpf.r-universe.devRecent package updates in marcelorpfR-universehttps://github.com/marcelorpf.pnghttps://marcelorpf.r-universe.devWed, 17 Jun 2026 19:04:09 GMTmarcelo@de.ufpb.br (Marcelo Rodrigo Portela Ferreira)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.https://github.com/r-universe/marcelorpf/actions/runs/27748727988Wed, 17 Jun 2026 19:04:09 GMTgkrreg0.4.0successhttps://marcelorpf.r-universe.devhttps://github.com/marcelorpf/gkrregintroduction.Rmdintroduction.htmlIntroduction to gkrreg: Gaussian Kernel Robust Regression2026-06-17 17:33:212026-06-17 17:33:21