Changes in version 0.1.0 (2026-06-18)                  

Initial release. The package implements the accuracy-level evaluation
metrics of Agustini, Fithriasari, and Prastyo (2026)
doi:10.1016/j.dajour.2025.100661.

Features

  - Four accuracy-level metrics on a 0-100% scale: Counted Squared Error
    (cse()), Counted Absolute Error (cae()), Counted Absolute Percentage
    Error (cape()), and Symmetric Counted Absolute Percentage Error
    (scape()), plus accuracy_level() for all four at once.
  - Baseline threshold tools: calculate_threshold() and
    auto_threshold(), with quartiles computed from the inverse empirical
    CDF (type = 1) to match the paper.
  - compare_models() implements the Figure 3 model-selection rule:
    Level-1 accuracy first, ties broken by the level's mean error (lower
    is better) before advancing to the next level. The comparison table
    reports accuracy and mean error per level.
  - Conventional and robust benchmarks: conventional_metrics()
    (R-squared, RMSE, NRMSE, MAE, MAPE, SMAPE), robust_metrics() (MedAE,
    trimmed MSE, Huber loss, quantile loss), and compare_all_metrics().
  - Framework integration: caret_summary() / caret_summary_extended() /
    caret_single_metric() for caret; cse_l1(), cae_l1(), cape_l1(),
    scape_l1(), accuracy_level_metrics(), and al_metric_set() for
    tidymodels/yardstick; al_forecast_accuracy(),
    al_compare_forecasts(), al_extended_accuracy(), and al_tsCV() for
    forecast.
  - vignette("replication") reproduces the simple-case (Table 4-6),
    regression-with-outlier, and time-series results, plus the
    caret/tidymodels/forecast integrations. The imputation case study is
    omitted because it relies on confidential firm-level microdata from
    BPS-Statistics Indonesia that cannot be redistributed.

Data and licensing

The package ships no datasets. The data used in the source article are
referenced by link rather than redistributed: the simple-regression and
candy-production series are public on Kaggle (the candy series
originates from the public-domain FRED series IPG3113N), while the firm
turnover microdata are confidential BPS-Statistics Indonesia survey
microdata and are not redistributable. Examples and the vignette use
small, reproducible simulated data generated inline.

Implementation notes

  - Perfect predictions (actual == predicted) give a zero baseline
    threshold; a machine-epsilon boundary is used so that exact-zero
    errors are assigned to Level 1.
  - Non-finite per-observation errors (for example absolute percentage
    error when an actual value is zero) are assigned to Level 4.
  - When a pre-computed threshold object is supplied to
    accuracy_level(), all four per-error-type thresholds derive from the
    stored baseline quartiles, so every model is evaluated against the
    same baseline (Figure 2 of the paper).