| balanced_acc | Confusion matrix, per-class recall and balanced accuracy |
| balanced_folds | Class-balanced cross-validation folds |
| compare_nsc | Compare homoscedastic and heteroscedastic NSC classifiers on one dataset |
| compute_rho2 | Empirical relative heterogeneity rho_k^2 and the crossover condition |
| hnsc | Fit a nearest shrunken centroid classifier with the threshold chosen by cross-validation |
| hnsc_cv | Cross-validation for a fitted NSC classifier |
| hnsc_methods | The six nearest shrunken centroid classifiers |
| hnsc_select | Choose the threshold by refined ("deep") cross-validation |
| hnsc_train | Train a nearest shrunken centroid classifier over a grid of thresholds |
| make_cm | Confusion matrix, per-class recall and balanced accuracy |
| per_class_recall | Confusion matrix, per-class recall and balanced accuracy |
| predict.hnsc | Predict with a nearest shrunken centroid classifier |
| recall_table | Summaries of a classifier comparison |
| simulate_hetero_data | Simulate two-class data with correlated informative genes |
| summary.hnsc_comparison | Summaries of a classifier comparison |