High-Performance Open-Source Archive
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.
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