This major release accompanies the publication of the simglm book. It expands the package’s simulation and power-analysis capabilities and aligns the package API with the examples presented in the book.
New outcome and data-generation options
- Added support for ordinal and multinomial outcomes.
- Added floor and ceiling controls for continuous outcomes.
- Changed factor simulation to preserve the declared order of levels rather than sorting levels alphabetically.
- Added
force_equal = TRUE for generating equally represented factor levels.
- Expanded support for unbalanced designs and flexible sample-size specifications.
- Generalized cross-classified data generation through multiple-membership simulation.
- Added post-processing and outcome aggregation tools.
- Extended formula parsing and simulation support for list-based specifications and multiple-equation workflows.
Power analysis and model fitting
- Reworked the power-simulation framework, including improved support for between- and within-subject designs.
- Added type S and type M error summaries.
- Added
robust_model() for models using robust standard errors.
Propensity score workflows
- Added
simulate_propensity() for generating non-random treatment assignment.
- Added
fit_propensity() and support for covariate adjustment, inverse probability weighting (ipw), and stabilized balancing weights (sbw).
- Added support, examples, and tests for multilevel propensity score designs.
Documentation, testing, and maintenance
- Expanded the vignettes and documentation for missing-data, factor, ordinal, multinomial, post-processing, power, and propensity score workflows.
- Added lightweight compatibility tests based on code published in the book.
- The package now requires R 4.1.0 or later because examples and simulation workflows use the native R pipe (|>)
- Removed
Matrix from package imports and added gtools, sandwich, and lmtest.
- Small maintenance fix for incoming 0.8 dplyr.
- Release for new tidy simulation framework
- New vignettes showing this functionality
- Add piecewise linear simulation.
- Add cross classified model simulation
- Add option to specify any model to fit for power analysis
- This brought about a change to use broom::tidy.
- Generalize fact_vars code
- This now is similar to cov_param
- Shiny Application works again!
- Can now simulate and run power.
- Able to download simulation and power tables (I think).
- Fixed basic functionality of Shiny application
- This includes simulation and power
- Needs more testing at this stage.
- Addition of count outcome from sim_glm.
- This added an additional argument that must be specified:
- outcome_type = ‘logistic’ = 0/1 dichotomous simulation
- outcome_type = ‘poisson’ = count outcomes.
- Bug fix for sim_glm when using fact_vars generation options.
- Heterogeneity of variance simulation
- Flexible time specification for longitudinal models
- Change ‘lvl’ to ‘level’ throughout package
- Flexible specification of unbalanced simulation
- Misspecification of model for power analysis
- Expand power output.
- Update to add ability to simulate covariates from any R distribution function
- Old code will no longer work with this new version.
- Added new opts argument to cov_param for optional distribution arguments.
- Adjusted vignettes to follow new code
- Adjusted unit tests.
- Added documentation for changes, including in vignettes.
- Added a
NEWS.md file to track changes to the package.