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