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.

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.

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.

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

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