Overview | Example data | Preparing data | Checking predictor types | Scaling predictors | Model choice | Model hierarchy | MAP: Maximum A Posteriori | Interpreting permutation output | Optimizer | max_iter | Class priors | weighted_avg in gipslda() | [S_ | [S_ | Formula interface | Matrix interface | Data-frame interface | Prediction output | Prediction methods for gipslda() | Leave-one-out prediction for QDA models | Inspecting fitted models | LDA diagnostics | Practical workflow | Troubleshooting | The model is slow | The output selects () | Predictions are identical across models | Training and test data have different preprocessing | Summary

Overview | Installation | Quick start | Preparing data | Model relationships | Choosing a model | Key arguments | MAP: Maximum A Posteriori | Optimizer | Fitting all three models | Inspecting a fitted model | Prediction output | Matrix interface | Summary

Purpose | Mock data in (\mathbb{R}^3) | Covariance projection | LDA reconstruction | Separate QDA reconstruction | Joint QDA reconstruction | Assertions