Changes in version 0.3.2 (2025-01-08)                  

New Features

  - fit_dd():
    
      - Introduced a new function to fit delay-discounting models using
        specified equations ("mazur"/"hyperbolic" or "exponential") and
        methods ("pooled", "mean", or "two stage").
      - Supports flexible data handling for aggregated and
        participant-specific modeling.
      - Returns an object of class "fit_dd" containing the fitted
        models, input data, and method details.

  - plot_dd():
    
      - Added a function to visualize fitted delay-discounting models.
      - Automatically adapts to different fitting methods, including
        aggregated and individual models.
      - Provides customizable axis labels, title, and optional
        log-transformed x-axis for improved visualization of delay
        scales.

  - results_dd():
    
      - New utility to extract model parameter estimates, confidence
        intervals, and fit statistics from a "fit_dd" object.
      - Supports both aggregated and participant-specific models.
      - Outputs a tidy tibble with columns for terms, estimates,
        standard errors, t-statistics, p-values, R2, three different AUC
        metrics, and confidence bounds.

  - check_unsystematic():
    
      - New utility function to check delay-discounting datasets for
        unsystematic data patterns according to Johnson & Bickel's
        (2008) two criteria.

  - calc_aucs():
    
      - New utility function to calculate three different area under the
        curve (AUC) metrics for delay-discounting data according to
        Borges et al. (2016).

Improvements

  - Confidence intervals are now computed using the calc_conf_int()
    function, ensuring accurate estimation based on model degrees of
    freedom.
  - R2 values are calculated consistently using the calc_r2() function,
    providing reliable fit metrics for all models.

Enhancements

  - The package now supports robust delay-discounting workflows, from
    unsystematic identification (check_unsystematic), model fitting
    (fit_dd), to visualization (plot_dd), to result extraction
    (results_dd).
  - Improved compatibility with delay-discounting datasets that require
    participant-level or aggregated modeling approaches.

                 Changes in version 0.3.1 (2023-11-16)                  

Minor fix

  - Correctly names output columns from calc_pd() and score_pd(). ep50
    changed to etheta50 and corrected calculation of ep50.

                 Changes in version 0.3.0 (2023-11-14)                  

New features

  - Add functions for scoring 5.5 trial probability discounting task
    (from the Qualtrics template) including: calc_pd() (and score_pd(),
    timing_pd(), and ans_pd).

Minor fix

  - Subsetting issue is fixed in score_dd() that would unintentionally
    drop all rows if both conditions were FALSE.

Other changes

  - Rename example data from five.fivetrial to five.fivetrial_dd for
    delay discounting.

  - Add example data five.fivetrial_pd for probability discounting.

                 Changes in version 0.2.0 (2023-11-02)                  

New features

  - score_mcq27() properly supports arguments: impute_method, random,
    return_data, and verbose. See documentation and the README for
    explanations.

  - generate_data_mcq() can generate fake MCQ data, including seed and
    prop_na arguments for reproducibility and specifying proportion of
    NAs.

  - long_to_wide* and wide_to_long* are helper functions to reshape data
    from/to different formats.

Minor fix

  - When no imputation is specified and NAs exist in the data,
    score_mcq27() returns NAs for the scoring instead of 1.

                 Changes in version 0.1.0 (2023-10-26)                  

  - Initial release with basic scoring of 27-item Monetary Choice
    Questionnaire and 5.5 trial delay discounting task from the
    Qualtrics template.

  - Added a NEWS.md file to track changes to the package.