# Holistic Multimodel Domain Analysis for Exploratory Machine Learning

## Documentation for package ‘HMDA’ version 0.3.0

- DESCRIPTION file
- Package README

## Help Pages

best_of_family | Select Best Models by Performance Metrics
check_efa | Check Exploratory Factor Analysis Suitability
dictionary | Dictionary of Variable Attributes
hmda.adjust.params | Adjust Hyperparameter Combinations
hmda.autoEnsemble | Build Stacked Ensemble Model Using autoEnsemble R package
hmda.best.models | Select Best Models Across All Models in HMDA Grid
hmda.compare.shap.plot | Compare SHAP plots across selected models
hmda.domain | Domain-level WMSHAP summary and plot
hmda.efa | Perform Exploratory Factor Analysis with HMDA
hmda.feature.selection | Feature Selection Based on Weighted SHAP Values
hmda.grid | Tune a Cartesian Hyperparameter Grid in HMDA
hmda.grid.analysis | Analyze Hyperparameter Grid Performance
hmda.init | Initialize or Restart H2O Cluster for HMDA Analysis
hmda.partition | Partition Data for HMDA Analysis
hmda.plot | Plot WMSHAP contributions
hmda.plot.metrics | Plot model performance metrics across a grid of models
hmda.row.plot | WMSHAP row-level plot for a single observation (participant or data row)
hmda.search.param | Search for Hyperparameters via Random Search
hmda.suggest.param | Suggest Hyperparameters for tuning HMDA Grids
hmda.test | Normalize a vector based on specified minimum and maximum values
hmda.wmshap | Compute Weighted Mean SHAP Values and Confidence Intervals via shapley algorithm
hmda.wmshap.table | Create SHAP Summary Table Based on the Given Criterion
suggest_mtries | Suggest Alternative mtries Values