MAIHDA 0.1.8

General Updates & New Features

• Added plot_prediction_deviation_panels() function for visualizing predicted values and identifying deviant cases.
• Added plot_risk_vs_effect() function to create a quadrant scatterplot comparing overall marginal predicted risk against pure intersectional effects.
• Added plot_effect_decomposition() function to visually decompose the total deviation from the overall mean into additive and intersectional components.
• Replaced the redundant “caterpillar” plot with the “predicted” plot in plot() and the interactive dashboard.
• Added automatic tertile binning (via an autobin parameter) for numeric grouping variables with more than 10 unique values in make_strata().
• Updated the interactive Shiny Dashboard (run_maihda_app()) to include the new visualizations and a toggle for auto-binning continuous strata variables.
• Added detection for binomial data. fit_maihda() will now automatically detect binomial outcomes and switch to the appropriate family.

Bug Fixes

• VPC/ICC Calculation Fix: Corrected the residual variance estimation for binomial and ordinal models. The package now accurately applies the theoretical level-1 variance (\(\pi^2 / 3\) for "logit" links and \(1\) for "probit" links) internally when summarizing models or bootstrapping the variance partition coefficient, avoiding deflated VPC/ICC metrics.

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() function for variance partition and stratum estimates
• Added predict_maihda() function for individual and stratum-level predictions
• Added plot() 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