# Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks

## Documentation for package ‘cutpointr’ version 1.0.32

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

## Help Pages

abs_d_ppv_npv | Calculate the absolute difference of positive and negative predictive value
abs_d_sens_spec | Calculate the absolute difference of sensitivity and specificity
accuracy | Calculate accuracy
acc_constrain | Metrics that are constrained by another metric
add_metric | Add metrics to a cutpointr or roc_cutpointr object
auc | Calculate AUC from a roc_cutpointr or cutpointr object
auc.cutpointr | Calculate AUC from a roc_cutpointr or cutpointr object
auc.roc_cutpointr | Calculate AUC from a roc_cutpointr or cutpointr object
boot_ci | Calculate bootstrap confidence intervals from a cutpointr object
boot_test | Test for equivalence of a metric
cohens_kappa | Calculate Cohen's Kappa
cutpoint | Extract the cutpoints from a ROC curve generated by cutpointr
cutpointr | Determine and evaluate optimal cutpoints
cutpointr.default | Determine and evaluate optimal cutpoints
cutpointr.numeric | Determine and evaluate optimal cutpoints
cutpointr_ | The standard evaluation version of cutpointr (deprecated)
cutpoints | Extract the cutpoints from a ROC curve generated by cutpointr
cutpoint_knots | Calculate number of knots to use in spline smoothing
F1_score | Calculate the F1-score
false_discovery_rate | Calculate the false omission and false discovery rate
false_omission_rate | Calculate the false omission and false discovery rate
fn | Extract number true / false positives / negatives
fnr | Calculate true / false positive / negative rate
fp | Extract number true / false positives / negatives
fpr | Calculate true / false positive / negative rate
Jaccard | Calculate the Jaccard Index
maximize_boot_metric | Optimize a metric function in binary classification after bootstrapping
maximize_gam_metric | Optimize a metric function in binary classification after smoothing via generalized additive models
maximize_loess_metric | Optimize a metric function in binary classification after LOESS smoothing
maximize_metric | Optimize a metric function in binary classification
maximize_spline_metric | Optimize a metric function in binary classification after spline smoothing
metric_constrain | Metrics that are constrained by another metric
minimize_boot_metric | Optimize a metric function in binary classification after bootstrapping
minimize_gam_metric | Optimize a metric function in binary classification after smoothing via generalized additive models
minimize_loess_metric | Optimize a metric function in binary classification after LOESS smoothing
minimize_metric | Optimize a metric function in binary classification
minimize_spline_metric | Optimize a metric function in binary classification after spline smoothing
misclassification_cost | Calculate the misclassification cost
multi_cutpointr | Calculate optimal cutpoints and further statistics for multiple predictors
nlr | Calculate the positive or negative likelihood ratio
npv | Calculate the negative predictive value
oc_manual | Set a manual cutpoint for use with cutpointr
oc_mean | Use the sample mean as cutpoint
oc_median | Use the sample median as cutpoint
oc_youden_kernel | Determine an optimal cutpoint maximizing the Youden-Index based on kernel smoothed densities
oc_youden_normal | Determine an optimal cutpoint for the Youden-Index assuming normal distributions
odds_ratio | Calculate the odds ratio
plot.cutpointr | Plot cutpointr objects
plot.multi_cutpointr | Plotting multi_cutpointr objects is currently not supported
plot.roc_cutpointr | Plot ROC curve from a cutpointr or roc_cutpointr object
plot_cutpointr | General purpose plotting function for cutpointr or roc_cutpointr objects
plot_cut_boot | Plot the bootstrapped distribution of optimal cutpoints from a cutpointr object
plot_metric | Plot a metric over all possible cutoffs from a cutpointr object
plot_metric_boot | Plot the bootstrapped metric distribution from a cutpointr object
plot_precision_recall | Precision recall plot from a cutpointr object
plot_roc | Plot ROC curve from a cutpointr or roc_cutpointr object
plot_roc.cutpointr | Plot ROC curve from a cutpointr or roc_cutpointr object
plot_roc.roc_cutpointr | Plot ROC curve from a cutpointr or roc_cutpointr object
plot_sensitivity_specificity | Sensitivity and specificity plot from a cutpointr object
plot_x | Plot the distribution of the independent variable per class from a cutpointr object
plr | Calculate the positive or negative likelihood ratio
ppv | Calculate the positive predictive value
precision | Calculate precision
predict.cutpointr | Predict using a cutpointr object
print.cutpointr | Print cutpointr objects
print.multi_cutpointr | Print multi_cutpointr objects
prod_ppv_npv | Calculate the product of positive and negative predictive value
prod_sens_spec | Calculate the product of sensitivity and specificity
prostate_nodal | Nodal involvement and acid phosphatase levels in 53 prostate cancer patients
p_chisquared | Calculate the p-value of a chi-squared test
recall | Calculate recall
risk_ratio | Calculate the risk ratio (relative risk)
roc | Calculate a ROC curve
roc01 | Calculate the distance between points on the ROC curve and (0,1)
sensitivity | Calculate sensitivity
sens_constrain | Metrics that are constrained by another metric
specificity | Calculate specificity
spec_constrain | Metrics that are constrained by another metric
suicide | Suicide attempts and DSI sum scores of 532 subjects
sum_ppv_npv | Calculate the sum of positive and negative predictive value
sum_sens_spec | Calculate the sum of sensitivity and specificity
tn | Extract number true / false positives / negatives
tnr | Calculate true / false positive / negative rate
total_utility | Calculate the total utility
tp | Extract number true / false positives / negatives
tpr | Calculate true / false positive / negative rate
user_span_cutpointr | Calculate bandwidth for LOESS smoothing of metric functions by rule of thumb
youden | Calculate the Youden-Index