B1000                   Balanced data with 1000 positives and 1000
                        negatives.
B500                    Balanced data with 500 positives and 500
                        negatives.
C3N150                  Multiclass sample with three classes.
IB1000                  Imbalanced data with 1000 positives and 10000
                        negatives.
IB500                   Imbalanced data with 500 positives and 5000
                        negatives.
M2N50F5                 5-fold cross validation sample.
P10N10                  A small example dataset with several tied
                        scores.
as.data.frame           Convert a curves and points object to a data
                        frame
as.data.table           Convert a curves and points object to a
                        data.table
auc                     Retrieve a data frame of AUC scores
auc_boot                Bootstrap AUCs from one test set
auc_ci                  Calculate CIs of ROC and precision-recall AUCs
auc_delong              DeLong's standard error for the ROC AUC
auc_diff                Compare AUCs between models
autoplot                Plot performance evaluation metrics with
                        ggplot2
average_precision       Calculate the average precision
best_cutoff             Choose an operating point
classification_report   Classification report
create_sim_samples      Create random samples for simulations
evalmod                 Evaluate models and calculate performance
                        evaluation metrics
format_nfold            Create n-fold cross validation dataset from
                        data frame
format_points           Reconstruct per-instance data from a table of
                        curve points
fortify                 Convert a curves and points object to a data
                        frame for ggplot2
join_labels             Join observed labels of multiple test datasets
                        into a list
join_scores             Join scores of multiple models into a list
metric_curve            Draw one evaluation metric against another
metric_table            Get every evaluation metric at every cutoff
mmdata                  Reformat input data for performance evaluation
                        calculation
part                    Calculate partial AUCs
pauc                    Retrieve a data frame of pAUC scores
plot                    Plot performance evaluation metrics
prbe                    Calculate the precision-recall break-even point
precrec                 precrec: A package for computing accurate ROC
                        and Precision-Recall curves
print                   Print the summary of a precrec object
prob_metrics            Calculate the Brier score, the RMSE and the log
                        loss of predicted probabilities
prob_metrics_ci         Calculate CIs of the Brier score and the log
                        loss
