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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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