Changes in version 0.8.0 (2026-06-09)                  

  - Added superlearner and sl imputers. These construct a Super
    Learner-style ensemble by cross-validating candidate imputers on
    observed cells, assigning non-negative loss-based weights, and
    combining predictions inside the existing chained-imputation loop.
  - Added library, folds, and metalearner hyperparameters for
    superlearner.
  - Updated CRAN preparation files and vignette examples for the new
    release.

                         Changes in version 0.7                         

First public release candidate.

  - Added ncore to impute() for completed-dataset-level parallel
    imputation through functionals::fmap().
  - Added lightweight iteration traces to mimar_imputation diagnostics
    for convergence screening.
  - Added diagnostic plot types for boxplots, bivariate observed/imputed
    comparisons, categorical proportions, and trace summaries.
  - Updated density diagnostics to draw line-only overlays across
    imputations so multiple completed datasets remain visible.
  - Refreshed the diagnostic plotting palette to give mimar a distinct
    visual identity while retaining the existing plot themes.
  - Expanded the vignette with KNN-based diagnostic examples, parallel
    imputation notes, and interpretation guidance.

                        Changes in version 0.0.1                        

  - Initial compact missing-data grammar.
  - Added description, amputation, imputation, evaluation, pooling, and
    plotting.
  - Added chained native and optional learner-backed imputation adapters
    without a funcml dependency.