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  "Title": "Gaussian Kernel Robust Regression (GKRReg)",
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  "Description": "Implements the Gaussian Kernel Robust Regression (GKRReg /\nGKRR) method proposed by De Carvalho, Lima Neto and Ferreira\n(2017) <doi:10.1016/j.neucom.2016.12.035>. The method\nre-weights observations iteratively using the Gaussian kernel\nso that poorly-fitted observations (outliers, leverage points)\nreceive small weights, yielding resistance to Y-space outliers,\nX-space outliers and leverage points. Convergence is guaranteed\nby Propositions 4.1 and 4.2 of the original paper. Three\nestimators for the kernel width hyper-parameter are provided\n(S1: Caputo, S2: pairwise median, S3: residual variance).\nInference is provided via an analytic sandwich variance\nestimator (default) or via bootstrap (percentile, normal and\nBCa intervals with p-values) through gkrr_boot(). Six real\ndatasets from the robust regression literature are included to\nfacilitate reproducible comparisons.",
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