Changes in version 0.2.0 (2026-07-09)                  

  - Added a calibration-inference layer for binary and multiclass
    predictions.
      - skce() computes the squared kernel calibration error of Widmann
        et al. (2019) with three estimators: the unbiased U-statistic
        ("uq"), the unbiased linear-time estimator ("ul"), and the
        biased V-statistic ("biased"). It supports the binary/confidence
        reduction, the strong (canonical) multiclass form via a
        matrix-valued kernel, and the classwise one-vs-rest reduction.
        In the binary and confidence cases mmce() equals sqrt(skce(...,
        estimator = "biased")).
      - cal_test() performs a kernel calibration hypothesis test (H0:
        the model is calibrated). The default method = "bootstrap" uses
        the more powerful quadratic estimator with a wild bootstrap
        (Widmann et al. 2019, Theorem G.2); method = "asymptotic" uses
        the faster linear-estimator normal test (Lemma 3). It returns an
        object of class c("cal_test", "htest"). The test targets are
        binary, "confidence", and the strong "canonical" multiclass
        form; the classwise average is available only as a point
        estimate from skce().
      - cal_ci() returns a percentile bootstrap confidence interval for
        ece(), skce(), mmce(), mce(), or ace(), as a classed cal_ci
        object with a print() method.
      - skce() and cal_test() accept bandwidth = "median" for the
        median-heuristic kernel scale (Widmann et al. 2019), recommended
        for the canonical multiclass form; the fixed 0.2 remains the
        default.
  - ece() gains debiased (the debiased squared-ECE estimator of Kumar,
    Liang & Ma 2019, Definition 5.2), strategy (equal-width or
    equal-mass bins, Roelofs et al. 2022), and norm ("l1" or "l2"). The
    norm and debiased choices are independent; debiasing is defined only
    for norm = "l2". Defaults (norm = "l1", debiased = FALSE, strategy =
    "width") reproduce the previous numeric output exactly.
  - stratified_folds() (used by cal_cv()) now scopes the optional
    fold-assignment seed with withr::local_seed() instead of touching
    .Random.seed directly; withr was added to Imports. The fold
    assignments for a given seed are unchanged.
  - inst/CITATION now reads the version from the package metadata, and
    the package author is also declared as copyright holder in
    Authors@R.
  - The README documents the calibration-inference layer (skce(),
    cal_test(), cal_ci(), and the debiased, strategy, and norm arguments
    of ece()) with a worked example.

                        Changes in version 0.1.0                        

  - CRAN submission version.

                     Changes in version 0.0.0.9000                      

  - Added multiclass calibration. cal_temperature() and cal_cv() accept
    a logit or probability matrix, and new constructors
    cal_vector_scaling(), cal_dirichlet(), and cal_ovr() cover vector
    scaling, Dirichlet calibration, and one-vs-rest calibration.
  - ece(), mce(), ace(), and reliability_diagram() accept a probability
    matrix with a type argument for classwise or top-label confidence
    evaluation.
  - Added mmce(), a binning-free Maximum Mean Calibration Error metric
    for binary and multiclass predictions.
  - Added inst/CITATION so users can cite the package with
    citation("probcal").
  - Added applied workflow, calibrator selection, and numerical
    validation vignettes.
  - print() and summary() respect options(probcal.emoji = FALSE) to
    suppress the decorative glyph in console output.
  - reliability_diagram() now reports ECE in the subtitle by default and
    can use either count-scaled or fixed-size points.
  - Added optional development validation tests against Python netcal
    and R betacal.
  - Initial development version with binary calibration methods,
    calibration metrics, reliability diagrams, and out-of-fold
    calibration support.