pROC and ROCR give different values for AUC

Packages pROC and ROCR both calculate/approximate the Area Under (Receiver Operator) Curve. However the results are different.

I am computing a new variable as a predictor for a label. The new variable is a (non-linear) function of a set of input values, and I'm checking how different parameter settings contribute to prediction. All my settings are predictive, but some are better.

The AUC i got with pROC was much lower then expected, so i tried ROCR. Here are some comparisons:
AUC from pROC         AUC from ROCR
0.49465                      0.79311
0.49465                      0.79349
0.49701                      0.79446
0.49701                      0.79764

When i draw the ROC (with pROC) i get the curve i expect. But why is the AUC according to pROC so different?

maintainer('ROCR')

maintainer('pROC')