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beezdemand 0.2.0

Deprecations

• FitCurves() is now superseded by fit_demand_fixed(). FitCurves() will continue to work but emits a soft deprecation warning. The new function provides a modern S3 interface with summary(), tidy(), glance(), predict(), and plot() methods. See vignette("migration-guide") for migration instructions.

• FitMeanCurves() is now superseded by fit_demand_fixed(agg = "Mean") or fit_demand_fixed(agg = "Pooled").

New Features

Koffarnus Equation for Mixed-Effects Models

• fit_demand_mixed() now supports the Koffarnus et al. (2015) exponentiated equation via equation_form = "koff". This enables fitting demand curves using the same equation form available in FitCurves() within the modern hierarchical mixed-effects framework. The k parameter can be user-specified or auto-calculated from data range.

broom Integration

• New augment() methods for all model classes provide fitted values and residuals in a tidy tibble:
  • augment.beezdemand_fixed(): Returns .fitted, .resid
  • augment.beezdemand_hurdle(): Returns .fitted, .fitted_link, .fitted_prob, .resid, .resid_response
  • augment.beezdemand_nlme(): Returns .fitted, .resid, .fixed

Model Comparison Framework

• New compare_models() function for unified model comparison across all beezdemand model classes. Reports AIC, BIC, delta_AIC, delta_BIC, and performs likelihood ratio tests when models are from the same backend and nested.

• New anova() S3 methods for comparing nested models:

  • anova.beezdemand_hurdle(): LRT for nested hurdle models
  • anova.beezdemand_nlme(): Delegates to nlme::anova.lme()

Model Diagnostics Suite

• New check_demand_model() generic with methods for all model classes. Performs comprehensive diagnostics including convergence checks, boundary condition detection, random effect variance assessment, and residual outlier detection. Returns structured diagnostics object with issues and recommendations. (Named check_demand_model() to avoid conflict with performance::check_model().)

• New plot_residuals() function creates diagnostic plots: residuals vs fitted, histogram of residuals, and Q-Q plots. Works with all model classes via the augment() infrastructure.

• New plot_qq() function creates Q-Q plots for random effects to assess normality assumptions in hurdle and NLME models.

Normalized Alpha (Alpha Star)

• All model classes now compute alpha_star (normalized alpha, Strategy B; Rzeszutek et al., 2025), which makes the elasticity parameter comparable across different values of k. Available in FitCurves() output (columns alpha_star and alpha_star_se), tidy() on beezdemand_fixed objects, and tidy() on beezdemand_hurdle objects. Standard errors are obtained via the delta method. See ?param-registry for details.

Modern Wrappers for Legacy Functions

• New get_empirical_measures() as a modern replacement for GetEmpirical(). Returns a beezdemand_empirical S3 object; access the results via $measures.

• New get_descriptive_summary() as a modern replacement for GetDescriptives(). Returns a beezdemand_descriptive S3 object; access the results via $statistics.

• New get_k() as a modern replacement for GetK(). Returns a single numeric k value with optional verbose output.

Other New Features

• New confint() methods for extracting confidence intervals from all model classes: beezdemand_fixed, beezdemand_hurdle, beezdemand_nlme, and cp_model_nls.

• New migration guide vignette (vignette("migration-guide")) documenting the transition from FitCurves() to fit_demand_fixed().

Breaking Changes

• summary() methods for beezdemand_hurdle and beezdemand_nlme now return structured summary objects instead of printing directly. Use print(summary(fit)) for console output. Programmatic access is now possible: s <- summary(fit); s$coefficients.

• fit_demand_hurdle() now fits demand parameters in natural-log space (log_q0, log_alpha, log_k) and reports back-transformed values; the param_space argument has been removed.

• fit_cp_nls() now uses log10-parameterized optimizer coefficients (log10_qalone, I, log10_beta) across equation forms; the "exponential" form fits on the log10(y) response scale and filters y <= 0 with a warning. predict.cp_model_nls() now always returns y_pred on the natural y scale; for "exponential" it additionally returns y_pred_log10 (and no longer returns y_pred_natural).

Additional New Features

• New fit_demand_fixed() function provides a modern interface for individual demand curve fitting. Returns a structured S3 object with summary(), tidy(), and glance() methods. This wrapper offers the same functionality as FitCurves() but with a standardized API.

• New systematicity wrappers with unified output vocabulary:

  • check_systematic_demand() for purchase task data (wraps CheckUnsystematic())
  • check_systematic_cp() for cross-price data (wraps check_unsystematic_cp())

  Both return beezdemand_systematicity objects with identical column schemas (differing only in NA values for domain-specific fields).

• First-class tidy() and glance() support is now guaranteed across all beezdemand model classes. All methods return tibbles with standardized columns including model_class and backend.

• All summary objects now inherit from beezdemand_summary base class, enabling shared fallback behavior and consistent field availability.

API Standardization

This release introduces Stability Contracts for all model classes:

• summary() objects now return structured S3 objects with class c("summary.<class>", "beezdemand_summary"). Required fields include: call, model_class, backend, nobs, n_subjects, converged, logLik, AIC, BIC, coefficients (tibble), notes.

• tidy() methods return tibbles with columns: term, estimate, std.error, statistic, p.value. Multi-part models include a component column (e.g., “fixed”, “variance”, “derived”).

• glance() methods return 1-row tibbles with columns: model_class, backend, nobs, n_subjects, converged, logLik, AIC, BIC.

API Changes

• fit_cp_nls() and fit_cp_linear() now accept x_var/y_var to map non-standard column names to canonical ones ("x", "y"). fit_cp_linear() additionally accepts id_var, group_var, and target_var. Default behavior is unchanged when these arguments are omitted.

• fit_cp_linear() gains explicit filter_target and target_level top-level arguments (previously these were handled implicitly via validate_cp_data()). Existing calls without these arguments are unaffected.

• fit_cp_nls(start_vals=) is deprecated in favor of start_values=. The old argument still works but emits a deprecation warning.

beezdemand 0.1.3

Deprecations

The following deprecations will take effect in version 0.2.0:

• beezdemand::pull() is deprecated in favor of dplyr::pull(). The beezdemand version was a legacy helper that predates the dplyr function.

• The inverse_fun argument in summary.cp_model_nls(), plot.cp_model_nls(), and predict.cp_model_nls() is deprecated in favor of inv_fun for consistency with mixed-effects model methods.

API Improvements

• Standardized argument names across cross-price model methods (inv_fun instead of inverse_fun)

• Cross-price plot methods now have consistent argument ordering across plot.cp_model_nls(), plot.cp_model_lm(), and plot.cp_model_lmer()

• Key user-facing functions now return tibbles for better compatibility with

  tidyverse workflows: predict.cp_model_nls(), predict.cp_model_lm(), tidy.cp_model_nls(), glance.cp_model_nls()

• Added standardized error helpers (validation_error(), fitting_error(), missing_package_error()) for consistent error messaging

• check_unsystematic_cp() now returns an object of class cp_unsystematic with proper summary() method dispatch (no longer overrides summary.tbl_df())

New Features

Two-Part Mixed Effects Hurdle Demand Models

• Added comprehensive hurdle model functionality using TMB (Template Model Builder):

  • fit_demand_hurdle(): Fit two-part hurdle models with 2 or 3 random effects

  • Part I models probability of zero consumption (logistic regression with random intercept)

  • Part II models log-consumption given positive response (nonlinear mixed effects)

• S3 methods for beezdemand_hurdle objects:

  • print(), summary(), coef(), logLik(), AIC(), BIC()

  • predict(): Extract subject parameters or predict demand/probability

  • plot(): Visualize demand curves, zero probability, parameter distributions

• Utility functions:

  • calc_omax_pmax(): Calculate Pmax and Omax from demand parameters

  • get_subject_pars(): Extract subject-specific parameter estimates

  • compare_hurdle_models(): Likelihood ratio test for model comparison

  • get_hurdle_param_summary(): Summary statistics for individual parameters

• Simulation functions:

  • simulate_hurdle_data(): Generate synthetic hurdle model data

  • run_hurdle_monte_carlo(): Monte Carlo simulation studies

• New vignette “Hurdle Demand Models” with comprehensive examples

• New dataset apt_full: Full alcohol purchase task data with 1,100 subjects and demographic covariates

Cross-Price Demand Models

• Added comprehensive cross-price demand model functionality:

  • check_unsystematic_cp(): Check for unsystematic data in cross-price models

  • fit_cp_nls(): Nonlinear model fitting for cross-price demand data

  • fit_cp_linear(): Linear model fitting for cross-price demand data with options for fixed effects and mixed-effects models

  • New utility functions for cross-price model objects:

    • summary(), plot(), glance(), and tidy() methods

  • extract_coefficients(): Extract model coefficients in tidy format

  • cp_posthoc_slopes() and cp_posthoc_intercepts(): Post-hoc comparisons for model parameters

  • validate_cp_data(): Validate and filter cross-price demand data

• Added new vignette “How to Use Cross-Price Demand Model Functions” demonstrating:

  • Required data structure for cross-price analyses

  • Checking for unsystematic data

  • Both two-stage and pooled model fitting approaches

  • Linear and mixed-effects modeling options

  • Model visualization and coefficient extraction

  • Post-hoc comparisons

beezdemand 0.1.2

• No longer relies on nlmrt and instead relies on nlsr

• Fixes an issue where CheckUnsystematic may not flag certain cases when data are passed as tibble

• Fixes deprecated arguments in ggplot2

• Add ability to specify a start value for alpha in ExtraF() function

beezdemand 0.1.1

• Add experimental features for FitCurves(). These arguments are constrainq0, startq0, and startalpha. These arguments allow Q0 to be constrained so alpha is the only fitted parameter and allow for user-specified starting values.

beezdemand 0.1.0

• Package successfully on CRAN!

beezdemand 0.1.00

• Package should be ready for CRAN and is being submitted

beezdemand 0.0.95

New updates

• One major change that might affect previous scripts is that in output summary tables, the column formally named ID is now named id (lowercase)

• Cleaned up a few things here and there. The package is close for submission to CRAN as it passes R CMD check with no errors, warnings, or notes

beezdemand 0.0.91

New updates

• GetSharedK() updated to work better and faster at finding a reasonable value

• Internal helper functions added to optimize GetSharedK()

beezdemand 0.0.90

New updates

• ExtraF() now compares alpha and Q0

• A number of functions now allows you to specify the column names

beezdemand 0.0.85

New updates

• FitCurves() correctly pulls alpha and q0 standard errors when k is fitted as a free parameter. Also no longer accepts data transformations. Must be done prior to fitting using ChangeData().

• FitCurves() now fits mean/pooled data based on method argument.

• GetSharedK() no longer accepts data transformations.

beezdemand 0.0.84

New updates

• New ChangeData() will soon serve as the replacement to ReplaceZeros() and other arguments specified in FitCurves().

• For the time being, FitCurves() will actually output a list object. This may cause failures with old scripts. Try modifying scripts to take the first element out of the list. This will soon be taken care of.

Tidying

• Email contact has been changed and some minor updates to .rd files.

beezdemand 0.0.6

New features

• New FitMeanCurves() will fit curve to averaged or pooled data. Can also make plots.

• FitCurves() can now make plots.

• GetDescriptives() can make box and whisker plots.

Cleanup

• Old code from previous workflow is removed. Now all functions use longform data.

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They may not be fully stable and should be used with caution. We make no claims about them.