Man pages for nestedcv
Nested Cross-Validation with 'glmnet' and 'caret'
| barplot_var_stability | Barplot variable stability |
| boot_filter | Bootstrap for filter functions |
| boot_ttest | Bootstrap univariate filters |
| boruta_filter | Boruta filter |
| boxplot_expression | Boxplot expression levels of model predictors |
| class_balance | Check class balance in training folds |
| coef.cva.glmnet | Extract coefficients from a cva.glmnet object |
| coef.nestcv.glmnet | Extract coefficients from nestcv.glmnet object |
| collinear | Filter to reduce collinearity in predictors |
| combo_filter | Combo filter |
| correls2 | Correlation between a vector and a matrix |
| cva.glmnet | Cross-validation of alpha for glmnet |
| cv_coef | Coefficients from outer CV glmnet models |
| cv_varImp | Extract variable importance from outer CV caret models |
| glmnet_coefs | glmnet coefficients |
| glmnet_filter | glmnet filter |
| innercv_preds | Inner CV predictions |
| innercv_roc | Build ROC curve from left-out folds from inner CV |
| innercv_summary | Summarise performance on inner CV test folds |
| lines.prc | Add precision-recall curve to a plot |
| lm_filter | Linear model filter |
| metrics | Model performance metrics |
| model.hsstan | hsstan model for cross-validation |
| nestcv.glmnet | Nested cross-validation with glmnet |
| nestcv.SuperLearner | Outer cross-validation of SuperLearner model |
| nestcv.train | Nested cross-validation for caret |
| one_hot | One-hot encode |
| outercv | Outer cross-validation of selected models |
| plot_alphas | Plot cross-validated glmnet alpha |
| plot_caret | Plot caret tuning |
| plot.cva.glmnet | Plot lambda across range of alphas |
| plot_lambdas | Plot cross-validated glmnet lambdas across outer folds |
| plot.prc | Plot precision-recall curve |
| plot_shap_bar | SHAP importance bar plot |
| plot_shap_beeswarm | SHAP importance beeswarm plot |
| plot_varImp | Variable importance plot |
| plot_var_stability | Plot variable stability |
| pls_filter | Partial Least Squares filter |
| prc | Build precision-recall curve |
| predict.cva.glmnet | Predict method for cva.glmnet models |
| predict.hsstan | Predict from hsstan model fitted within cross-validation |
| predict.nestcv.glmnet | Predict method for nestcv.glmnet fits |
| pred_nestcv_glmnet | Prediction wrappers to use fastshap with nestedcv |
| predSummary | Summarise prediction performance metrics |
| randomsample | Oversampling and undersampling |
| ranger_filter | Random forest ranger filter |
| relieff_filter | ReliefF filter |
| repeatcv | Repeated nested CV |
| repeatfolds | Create folds for repeated nested CV |
| rf_filter | Random forest filter |
| smote | SMOTE |
| stat_filter | Univariate filter for binary classification with mixed... |
| summary_vars | Summarise variables |
| supervisedPCA | Supervised PCA plot |
| train_preds | Outer training fold predictions |
| train_roc | Build ROC curve from outer CV training folds |
| train_summary | Summarise performance on outer training folds |
| ttest_filter | Univariate filters |
| txtProgressBar2 | Text Progress Bar 2 |
| var_direction | Variable directionality |
| var_stability | Variable stability |
| weight | Calculate weights for class imbalance |
