| precrec-package | precrec: A package for computing accurate ROC and Precision-Recall curves |
| as.data.frame | Convert a curves and points object to a data frame |
| as.data.frame.aucroc | Convert a curves and points object to a data frame |
| as.data.frame.mmcurves | Convert a curves and points object to a data frame |
| as.data.frame.mmpoints | Convert a curves and points object to a data frame |
| as.data.frame.mmxycurves | Convert a curves and points object to a data frame |
| as.data.frame.mscurves | Convert a curves and points object to a data frame |
| as.data.frame.mspoints | Convert a curves and points object to a data frame |
| as.data.frame.msxycurves | Convert a curves and points object to a data frame |
| as.data.frame.smcurves | Convert a curves and points object to a data frame |
| as.data.frame.smpoints | Convert a curves and points object to a data frame |
| as.data.frame.smxycurves | Convert a curves and points object to a data frame |
| as.data.frame.sscurves | Convert a curves and points object to a data frame |
| as.data.frame.sspoints | Convert a curves and points object to a data frame |
| as.data.frame.ssxycurves | Convert a curves and points object to a data frame |
| as.data.table | Convert a curves and points object to a data.table |
| as.data.table.aucroc | Convert a curves and points object to a data.table |
| as.data.table.mmcurves | Convert a curves and points object to a data.table |
| as.data.table.mmpoints | Convert a curves and points object to a data.table |
| as.data.table.mscurves | Convert a curves and points object to a data.table |
| as.data.table.mspoints | Convert a curves and points object to a data.table |
| as.data.table.smcurves | Convert a curves and points object to a data.table |
| as.data.table.smpoints | Convert a curves and points object to a data.table |
| as.data.table.sscurves | Convert a curves and points object to a data.table |
| as.data.table.sspoints | Convert a curves and points object to a data.table |
| auc | Retrieve a data frame of AUC scores |
| auc.aucs | 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_ci.aucboot | Calculate CIs of ROC and precision-recall AUCs |
| auc_ci.aucdelong | Calculate CIs of ROC and precision-recall AUCs |
| auc_ci.aucs | Calculate CIs of ROC and precision-recall AUCs |
| auc_delong | DeLong's standard error for the ROC AUC |
| auc_diff | Compare AUCs between models |
| auc_diff.aucboot | Compare AUCs between models |
| auc_diff.aucdelong | Compare AUCs between models |
| autoplot | Plot performance evaluation metrics with ggplot2 |
| autoplot.mmcurves | Plot performance evaluation metrics with ggplot2 |
| autoplot.mmpoints | Plot performance evaluation metrics with ggplot2 |
| autoplot.mmxycurves | Plot performance evaluation metrics with ggplot2 |
| autoplot.mscurves | Plot performance evaluation metrics with ggplot2 |
| autoplot.mspoints | Plot performance evaluation metrics with ggplot2 |
| autoplot.msxycurves | Plot performance evaluation metrics with ggplot2 |
| autoplot.smcurves | Plot performance evaluation metrics with ggplot2 |
| autoplot.smpoints | Plot performance evaluation metrics with ggplot2 |
| autoplot.smxycurves | Plot performance evaluation metrics with ggplot2 |
| autoplot.sscurves | Plot performance evaluation metrics with ggplot2 |
| autoplot.sspoints | Plot performance evaluation metrics with ggplot2 |
| autoplot.ssxycurves | Plot performance evaluation metrics with ggplot2 |
| average_precision | Calculate the average precision |
| average_precision.aucs | Calculate the average precision |
| B1000 | Balanced data with 1000 positives and 1000 negatives. |
| B500 | Balanced data with 500 positives and 500 negatives. |
| best_cutoff | Choose an operating point |
| C3N150 | Multiclass sample with three classes. |
| 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 |
| fortify.mmcurves | Convert a curves and points object to a data frame for ggplot2 |
| fortify.mmpoints | Convert a curves and points object to a data frame for ggplot2 |
| fortify.mmxycurves | Convert a curves and points object to a data frame for ggplot2 |
| fortify.mscurves | Convert a curves and points object to a data frame for ggplot2 |
| fortify.mspoints | Convert a curves and points object to a data frame for ggplot2 |
| fortify.msxycurves | Convert a curves and points object to a data frame for ggplot2 |
| fortify.smcurves | Convert a curves and points object to a data frame for ggplot2 |
| fortify.smpoints | Convert a curves and points object to a data frame for ggplot2 |
| fortify.smxycurves | Convert a curves and points object to a data frame for ggplot2 |
| fortify.sscurves | Convert a curves and points object to a data frame for ggplot2 |
| fortify.sspoints | Convert a curves and points object to a data frame for ggplot2 |
| fortify.ssxycurves | Convert a curves and points object to a data frame for ggplot2 |
| IB1000 | Imbalanced data with 1000 positives and 10000 negatives. |
| IB500 | Imbalanced data with 500 positives and 5000 negatives. |
| join_labels | Join observed labels of multiple test datasets into a list |
| join_scores | Join scores of multiple models into a list |
| M2N50F5 | 5-fold cross validation sample. |
| 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 |
| P10N10 | A small example dataset with several tied scores. |
| part | Calculate partial AUCs |
| part.mmcurves | Calculate partial AUCs |
| part.mscurves | Calculate partial AUCs |
| part.smcurves | Calculate partial AUCs |
| part.sscurves | Calculate partial AUCs |
| pauc | Retrieve a data frame of pAUC scores |
| pauc.aucs | Retrieve a data frame of pAUC scores |
| plot | Plot performance evaluation metrics |
| plot.mmcurves | Plot performance evaluation metrics |
| plot.mmpoints | Plot performance evaluation metrics |
| plot.mmxycurves | Plot performance evaluation metrics |
| plot.mscurves | Plot performance evaluation metrics |
| plot.mspoints | Plot performance evaluation metrics |
| plot.msxycurves | Plot performance evaluation metrics |
| plot.smcurves | Plot performance evaluation metrics |
| plot.smpoints | Plot performance evaluation metrics |
| plot.smxycurves | Plot performance evaluation metrics |
| plot.sscurves | Plot performance evaluation metrics |
| plot.sspoints | Plot performance evaluation metrics |
| plot.ssxycurves | Plot performance evaluation metrics |
| prbe | Calculate the precision-recall break-even point |
| prbe.curve_info | Calculate the precision-recall break-even point |
| precrec | precrec: A package for computing accurate ROC and Precision-Recall curves |
| Print the summary of a precrec object | |
| print.aucroc | Print the summary of a precrec object |
| print.beval_info | Print the summary of a precrec object |
| print.classification_report | Print the summary of a precrec object |
| print.curve_info | Print the summary of a precrec object |
| print.mdat | Print the summary of a precrec object |
| print.xycurve_info | 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 |