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pROC (version 1.7.1)

display and analyze ROC curves

Description

Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.

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Version

Install

install.packages('pROC')

Monthly Downloads

138,160

Version

1.7.1

License

GPL (>= 3)

Maintainer

Xavier Robin

Last Published

February 21st, 2014

Functions in pROC (1.7.1)

cov.roc

Covariance of two paired ROC curves
ci.sp

Compute the confidence interval of specificities at given sensitivities
roc

Build a ROC curve
has.partial.auc

Does the ROC curve have a partial AUC?
lines.roc

Add a ROC line to a ROC plot
plot.ci

Plot confidence intervals
ci.auc

Compute the confidence interval of the AUC
roc.test

Compare the AUC of two ROC curves
multiclass.roc

Multi-class AUC
ci.coords

Compute the confidence interval of arbitrary coordinates
var.roc

Variance of a ROC curve
aSAH

Subarachnoid hemorrhage data
ci.thresholds

Compute the confidence interval of thresholds
smooth.roc

Smooth a ROC curve
coords

Coordinates of a ROC curve
are.paired

Are two ROC curves paired?
print

Print a ROC curve object
plot.roc

Plot a ROC curve
power.roc.test

Sample size and power computation for ROC curves
auc

Compute the area under the ROC curve
pROC-package

pROC
ci

Compute the confidence interval of a ROC curve
groupGeneric

pROC Group Generic Functions
ci.se

Compute the confidence interval of sensitivities at given specificities