Man pages for HMDA
Holistic Multimodel Domain Analysis for Exploratory Machine Learning
| 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.fastcompute.globalshap | Fast Computation of Global SHAP Values |
| hmda.fastcompute.globalwmshap | Fast Computation of Global WMSHAP Values |
| 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.rashomon.set | Select a Near-Optimal from a Model Grid |
| hmda.row.plot | WMSHAP row-level plot for a single observation (participant... |
| 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... |
| hmda.wmshap | Compute Weighted Mean SHAP Values and Confidence Intervals... |
| hmda.wmshap.table | Create SHAP Summary Table Based on the Given Criterion |
| suggest_mtries | Suggest Alternative mtries Values |
