Introduction | Example: Fitting a rule ensemble | Computing marginal SHAP values | Visualizing marginal SHAP values | Visualizing marginal SHAP values with beeswarms and waterfall plots using package shapviz | Computing interaction SHAP values | Understanding (interaction) SHAP values | Visualizing interaction SHAP values | Session info | References

Introduction | Example: Predicting wind speed | Listwise Deletion | Single Imputation | Multiple Imputation | Stacking imputed datasets | Pooling | Comparing accuracy and sparsity | Session info | References

Introduction | Relaxed lasso | Adaptive lasso | Relaxed adaptive lasso | Example: Predicting Ozone levels with relaxed and/or adaptive lasso | Forward stepwise selection with the relaxed lasso | Conclusion | Session info | References

Introduction | Tuning parameters | Tuning parameters using package caret | Using Breiman's random forests for rule generation | References

Package summary | What is multi-view stacking? | Why package mvs? | Overview of package functionality | Model fitting | Parallelization | Model generalizations | View importance | Handling missing data | Using mvs step-by-step | Installation | Fitting a basic model | Random forests | Parallel computing | Fitting a model with more than two levels | Fitting a model with missing data | Acknowledgements | References

Faster rule generation | Tree fitting algorithm | Maximum rule depth | Number of trees | Faster estimation of the final ensemble | Parallel computation | Number of cross-validation folds | References

Introduction | Fitting a mixed-model tree with constant fits | Detecting treatment-subgroup interactions in clustered data | Detecting subgroups with different growth trajectories | R code for generating artificial motivating dataset

Introduction | Fitting a mixed-model tree with constant fits | Detecting treatment-subgroup interactions in clustered data | Detecting subgroups with different growth trajectories | R code for generating artificial motivating dataset