beezdiscounting 0.3.2

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.