                 Changes in version 0.1.0 (2026-09-15)                  

  - Initial public development release.
  - Added Type-1 Fuzzy Function models for binary classification and
    numeric regression using fuzzy C-means and membership-weighted local
    predictions.
  - Added formula and column interfaces, categorical predictor encoding,
    feature scaling, and explicit missing-value handling.
  - Added automatic separation detection, dependency-free ridge-logistic
    fallback, and configurable probability clipping.
  - Added validation, K-fold, and stratified K-fold hyperparameter
    tuning with task-appropriate metrics.
  - Added MAPE and SMAPE regression metrics to tuning, evaluation, and
    nested benchmarking, with explicit handling of zero actual values.
  - Added efficient joint tuning of cluster count, fuzziness, and
    classification threshold for threshold-dependent metrics.
  - Added model evaluation and repeated nested cross-validation with
    matched logistic or linear regression baselines.
  - Added optional probabilistic SVM and epsilon-SVR learners within
    fuzzy clusters via local_model = "svm".
  - Tightened public-input validation for integer resampling controls,
    logical scaling flags, classification labels, and validation split
    sizes.
  - Ensured unresolved final-model GLM instability warnings remain
    visible after tuning while routine candidate-fit warnings stay
    contained.
  - Added a comprehensive vignette and PDF reference manual.
