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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.

beezdiscounting 0.3.1

Minor fix

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

beezdiscounting 0.3.0

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.

beezdiscounting 0.2.0

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.

beezdiscounting 0.1.0

  • 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.