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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