sigma_method = "s4": new width hyper-parameter estimator based on the
AICc-selected bandwidth from sm::h.select() (squared to obtain a
variance-scale estimate of gamma^2). Recommended for large samples
(n >= 200). Requires the sm package (now in Imports).
sigma_method = "auto": automatic selection among S1, S2, S3 and S4 by
out-of-bag bootstrap MSE. The number of replicates and seed are
configurable via the new auto_args argument (e.g.,
auto_args = list(B = 99, seed = 1)). A message() is emitted
informing the user of the selected method and comparative OOB MSEs.
New auto_args argument in gkrr() for controlling the "auto"
selection bootstrap (default B = 99).
weighted argument has been removed from gkrr_boot(). The
weighted bootstrap was found to produce wider confidence intervals than
the standard pairs bootstrap in all tested scenarios, because the
robustness of GKRReg already resides in the kernel weights — the
bootstrap itself does not need to replicate this. The standard pairs
bootstrap is the recommended and only available option.Imports (previously not a dependency).summary() for inference (standard errors, confidence
intervals and Wald z-tests) when no bootstrap object is available.vcov() method added, returning the sandwich covariance matrix.summary() gains a se_tol argument controlling the threshold for
divergence warnings between sandwich and bootstrap standard errors.summary() emits a proactive note suggesting bootstrap inference when
small sample size (n < 50) or heavy contamination is detected.par() settings are now properly restored in all plot methods and
vignette chunks using oldpar <- par(no.readonly = TRUE) /
on.exit(par(oldpar)).First public release.
gkrr() fits a Gaussian Kernel Robust Regression model via IRWLS.
Three estimators for the kernel width hyper-parameter: "s1" (Caputo),
"s2" (pairwise median) and "s3" (residual variance).gkrr_boot() runs a pairs bootstrap to produce standard errors,
confidence intervals (percentile, normal, BCa) and p-values.boot argument in gkrr(): set boot = TRUE to compute bootstrap
inference at fit time.summary.gkrr() prints a coefficient table modelled after summary.lm().plot.gkrr() provides six diagnostic panels where point size is
inversely proportional to the kernel weight.plot.gkrr_boot() provides histogram and scatter-plot matrix panels.belgium_calls, cloud_point, kootenay,
delivery, mammals, stars_cyg.