# Calculate Accurate Precision-Recall and ROC (Receiver Operator Characteristics) Curves

## Documentation for package ‘precrec’ version 0.14.4

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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.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.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.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
auc | Retrieve a data frame of AUC scores
auc.aucs | Retrieve a data frame of AUC scores
auc_ci | Calculate CIs of ROC and precision-recall AUCs
auc_ci.aucs | Calculate CIs of ROC and precision-recall AUCs
autoplot | Plot performance evaluation measures with ggplot2
autoplot.mmcurves | Plot performance evaluation measures with ggplot2
autoplot.mmpoints | Plot performance evaluation measures with ggplot2
autoplot.mscurves | Plot performance evaluation measures with ggplot2
autoplot.mspoints | Plot performance evaluation measures with ggplot2
autoplot.smcurves | Plot performance evaluation measures with ggplot2
autoplot.smpoints | Plot performance evaluation measures with ggplot2
autoplot.sscurves | Plot performance evaluation measures with ggplot2
autoplot.sspoints | Plot performance evaluation measures with ggplot2
B1000 | Balanced data with 1000 positives and 1000 negatives.
B500 | Balanced data with 500 positives and 500 negatives.
create_sim_samples | Create random samples for simulations
evalmod | Evaluate models and calculate performance evaluation measures
format_nfold | Create n-fold cross validation dataset from data frame
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.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.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.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
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.
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 measures
plot.mmcurves | Plot performance evaluation measures
plot.mmpoints | Plot performance evaluation measures
plot.mscurves | Plot performance evaluation measures
plot.mspoints | Plot performance evaluation measures
plot.smcurves | Plot performance evaluation measures
plot.smpoints | Plot performance evaluation measures
plot.sscurves | Plot performance evaluation measures
plot.sspoints | Plot performance evaluation measures
precrec | precrec: A package for computing accurate ROC and Precision-Recall curves