Performance
measures that ROCR knows: Accuracy, error rate, true positive rate, false positive rate, true negative rate, false negative rate, sensitivity, specificity, recall, positive predictive value, negative predictive value, precision, fallout, miss, phi correlation coefficient, Matthews correlation coefficient, mutual information, chi square statistic, odds ratio, lift value, precision/recall F measure, ROC convex hull, area under the ROC curve, precision/recall breakeven point, calibration error, mean crossentropy, root mean squared error, SAR measure, expected cost, explicit cost. 
ROCR
features: ROC curves, precision/recall plots, lift charts, cost curves, custom curves by freely selecting one performance measure for the x axis and one for the y axis, handling of data from crossvalidation or bootstrapping, curve averaging (vertically, horizontally, or by threshold), standard error bars, box plots, curves that are colorcoded by cutoff, printing threshold values on the curve, tight integration with Rs plotting facilities (making it easy to adjust plots or to combine multiple plots), fully customizable, easy to use (only 3 commands). 
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