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pROC (version 1.7.2)

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

1.7.2

License

GPL (>= 3)

Maintainer

Xavier Robin

Last Published

April 6th, 2014

Functions in pROC (1.7.2)

ci.auc

Compute the confidence interval of the AUC
are.paired

Are two ROC curves paired?
ci.se

Compute the confidence interval of sensitivities at given specificities
ci.coords

Compute the confidence interval of arbitrary coordinates
pROC-package

pROC
cov.roc

Covariance of two paired ROC curves
roc.test

Compare the AUC of two ROC curves
groupGeneric

pROC Group Generic Functions
has.partial.auc

Does the ROC curve have a partial AUC?
aSAH

Subarachnoid hemorrhage data
auc

Compute the area under the ROC curve
smooth.roc

Smooth a ROC curve
plot.ci

Plot confidence intervals
plot.roc

Plot a ROC curve
lines.roc

Add a ROC line to a ROC plot
ci

Compute the confidence interval of a ROC curve
roc

Build a ROC curve
print

Print a ROC curve object
ci.thresholds

Compute the confidence interval of thresholds
ci.sp

Compute the confidence interval of specificities at given sensitivities
var.roc

Variance of a ROC curve
coords

Coordinates of a ROC curve
power.roc.test

Sample size and power computation for ROC curves
multiclass.roc

Multi-class AUC