Changes in version 0.2.0 (2026-05-12)                  

This is a large update, including complete refactoring of underlying
mechanisms related to how the package handles experimental designs and
the addition of mean centering as well as p-value adjustment. The way
study designs are defined has been changed in a major way, which also
led to changes of other functions in the package to work correctly with
the new functionality. As a result, the process of doing power analyses
should now be more streamlined and intuitive as well as support a larger
variety of research designs. The vignette has also been adapted to
reflect these changes.

Breaking Changes

  - The id parameter in define_design has been removed. Grouping
    variables and hierarchical structures are now defined entirely by a
    new sample_size parameter, providing a much cleaner and more
    intuitive syntax
  - The between parameter in define_design() has been changed. Now, the
    predictor can directly reference the desired analysis unit defined
    in sample_size
  - Some functions were changed in order to support the new mean
    centering functionality:
      - fixed_effects_from_average_outcome now has a new function
        parameter center. It defaults to TRUE and controls how the
        centering of predictors is handled. See function documentation
        for more details
      - The n_is_total in power_sim has been removed. This choice has
        been made obsolete by the new sample_size parameter in
        define_design
      - The plot_sim_model function now also has a center parameter that
        works identically to the one added in power_sim
  - In cases where users supply the fixed effects manually to power_sim
    (i.e. do not use fixed_effects_from_average_outcome) and an
    interaction effect is present in the formula, power_sim now stops
    execution and alerts the user to explicitly choose if centering
    should be applied

New Features & Parameters

  - Added automated p-value adjustment (Benjamini-Hochberg by default)
    in power_sim when testing multiple effects for power simultaneously.
    See function documentation for details
  - Added a mean centering functionality for predictors. Users can now
    configure if the predictors should be centered in the power_sim
    function PowRPriori automatically chooses either grand-mean
    centering or within-cluster centering (between-subject predictors
    are centered on the grand mean, within-subject predictors are
    centered on the cluster mean). See function documentation and
    vignette for details
  - power_sim has new parameters: adjust_p_value, along and center
      - adjust_p_value controls the method of p-value adjustment when
        multiple parameters are specified in test_parameter
      - along defines which variable specified in the sample_size
        parameter in define_design should be incremented after each
        simulation course (defaults to NULL, in which case the function
        increments the lowest analysis unit)
      - center controls if mean centering should be applied to the
        predictors. Defaults to auto, in which case the simulation
        engine tries to detect the appropriate centering method
  - Improved the underlying data simulation engine (updated
    .create_design_matrix and implemented a new function
    .center_predictors) to automatically and robustly handle complex
    hierarchical and crossed designs, as well as automatic
    mean-centering
  - Added a new parameter n to plot_sim_model, which allows the
    configuration of the sample size to be simulated for the plot and
    defaults to the sample size of the lowest analysis level otherwise
  - Added a new internal function .center_predictors which handles the
    predictor mean centering

General improvements / changes

  - The internal .create_design_matrix was updated to correctly handle
    the new objects created by define_design
  - Updated the vignette to reflect and incorporate all changes and new
    features

Bug fixes

  - Fixed an issue where plot_sim_model calculated the wrong average
    sample value for the data when using type="data"

                 Changes in version 0.1.2 (2026-01-28)                  

This a smaller update, improving the vignette and fixing a critical bug
in the functions providing code snippets for model specification as well
as some smaller bugs.

Bug fixes

Critical bug fix

  - Fixed incorrect handling of certain nested designs in
    get_fixed_effects_structrue and get_random_effects_structure so that
    the functions now consistently produce the correct code snippets

Miscellaneous smaller bugs

  - Fixed plotting of sample data where the incorrect regression lines
    were drawn when using plot_sim_model with type = "data"
  - Corrected the possible values of the type parameter in the
    plot_sim_model function when using it with an lme4-style formula
    object
  - Fixed incorrect console output when the data simulation with
    power_sim reached or exceeded max_simulation_steps
  - Fixed a bug where the simulation stop condition did not work
    correctly in some cases where more than the defined
    n_issue_stop_prop proportion of models had fitting issues

General improvements

  - Vastly extended the vignette to provide more background information
    on the functions, include more additional use-cases and further
    improve the overall readability
  - Improved the output of power_sim to include more information when
    simulating a nested design
  - Improved the output of power_sim to incorporate the percentage of
    model fits with issues in relation to the total number of models fit

                 Changes in version 0.1.1 (2025-12-14)                  

  - Initial CRAN submission