Changes in version 0.4.0 (2026-06-17)                  

New features

  - 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).

Removed features

  - The 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.

Other changes

  - sm added to Imports (previously not a dependency).

                        Changes in version 0.3.0                        

New features

  - Sandwich variance estimator (HC0) is now computed automatically at
    fit time and used by 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.

Bug fixes

  - par() settings are now properly restored in all plot methods and
    vignette chunks using oldpar <- par(no.readonly = TRUE) /
    on.exit(par(oldpar)).

                        Changes in version 0.2.0                        

First public release.

New features

  - 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.
  - Six real datasets bundled: belgium_calls, cloud_point, kootenay,
    delivery, mammals, stars_cyg.