MAIHDA 0.1.7

General Updates & New Features

• Added stepwise_pcv() function to sequentially estimate proportional change in variance (PCV) by adding predictors one-by-one.
• Added a fully-featured interactive Shiny Dashboard (via run_maihda_app()) for visual data exploration, model fitting, and performance visualization.
• Improved bootstrap methods for more efficient confidence interval estimation.
• Added missing documentation block for the maihda_sim_data dataset to resolve R CMD check warnings.
• Updated test suite setup: tests/testthat.R was modified to correctly use test_check("MAIHDA") instead of shinytest2.
• Added importFrom(stats, as.formula) for the stepwise_pcv function to prevent undefined warnings.
• Updated introduction.Rmd vignette: added standard CRAN installation instructions, and improved text clarity.

MAIHDA 0.1.0

Initial Release

• Initial CRAN submission
• Added make_strata() function for creating intersectional strata
• Added fit_maihda() function for fitting multilevel models with lme4 (default) or brms engines
• Added summary_maihda() function for variance partition and stratum estimates
• Added predict_maihda() function for individual and stratum-level predictions
• Added plot_maihda() function with three plot types:
  • Caterpillar plots of stratum random effects
  • Variance partition coefficient visualization
  • Observed vs. shrunken estimates comparison
• Added compare_maihda() function for comparing models with bootstrap confidence intervals
• Added comprehensive documentation and vignettes
• Added unit tests for core functionality

Bug Fixes and Improvements

• Enhanced make_strata() to properly handle missing values (NA) in input variables:
  • Observations with missing values in any stratum variable are now assigned NA stratum
  • Missing values are no longer included as valid stratum categories
  • Added comprehensive tests for missing value handling