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pROC (version 1.6.0.1)

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

1.6.0.1

License

GPL (>= 3)

Maintainer

Xavier Robin

Last Published

December 28th, 2013

Functions in pROC (1.6.0.1)

coords

Coordinates of a ROC curve
ci.thresholds

Compute the confidence interval of thresholds
power.roc.test

Sample size and power computation for ROC curves
var.roc

Variance of a ROC curve
auc

Compute the area under the ROC curve
print

Print a ROC curve object
ci.auc

Compute the confidence interval of the AUC
ci

Compute the confidence interval of a ROC curve
pROC-package

pROC
lines.roc

Add a ROC line to a ROC plot
plot.roc

Plot a ROC curve
ci.se

Compute the confidence interval of sensitivities at given specificities
aSAH

Subarachnoid hemorrhage data
roc

Build a ROC curve
has.partial.auc

Does the ROC curve have a partial AUC?
ci.sp

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

Compute the confidence interval of arbitrary coordinates
smooth.roc

Smooth a ROC curve
are.paired

Are two ROC curves paired?
plot.ci

Plot confidence intervals
cov.roc

Covariance of two paired ROC curves
multiclass.roc

Multi-class AUC
roc.test

Compare the AUC of two ROC curves