Changes in version 0.1.1                        

  - Added the World Happiness Report demonstration objects whr_raw,
    whr_calibrated, and whr_calib_spec.
  - Fixed calibration trace plots so plot(..., type = "trace") no longer
    duplicates an evaluated failing threshold.

                 Changes in version 0.1.0 (2026-05-27)                  

This is the first public version of qcaERT.

Package scope

  - Added a family of robustness tools for QCA workflows:
      - calib.test() for calibration-threshold robustness.
      - incl.test() for inclusion-cutoff robustness.
      - ncut.test() for frequency-cutoff robustness.
      - loo.test() for leave-one-out case influence.
      - subsample.test() for repeated subsample stability.
      - altset.test() for sampled alternative analysis settings.
      - theory.test() for comparing theoretically motivated
        condition-set specifications.
      - cluster.test() for cluster, group, and repeated-unit
        heterogeneity.
      - sol.df() for compact QCA solution tables.
      - sol.chart() for visual presentation of sol.df() tables.

Shared API and output conventions

  - Standardized the function family around common solution controls:
    solution, include, dir.exp, which_M, and i_mode.
  - Standardized exclusion handling across applicable functions with
    exclude_mode, exclude_recompute, and exclude_static.
  - Standardized most result objects around:
      - diagnostics for detailed/internal results.
      - results for the clean table.
      - settings for the analysis settings.
      - supporting fields such as baseline, bounds, by_direction,
        by_case, by_run, by_draw, and summary where relevant.
  - Standardized print() methods to show concise summaries.
  - Standardized as.data.frame() methods to return the clean table.
  - Documented the structured-result exceptions explicitly:
      - cluster.test() returns results$overview, results$clusters, and
        results$units; as.data.frame() returns results$overview.
      - theory.test() returns results$models, results$solutions, and
        results$pairwise; as.data.frame() returns results$models.