After years of work with ROC curves in R, first with packages such as ROCR and verification, then with custom functions; and 4 months of development to create a coherent package, extensive discussions about its name (SwissROC was the first thought (but is Switzerland still a selling argument?), or Rocker (that's a bit similar to the already-existing ROCR)) and its licensing (let's go with GPL), I'm glad to announce the release of pROC!

An example of ROC analysis with pROC.

pROC is a set of tools to visualize, smooth and compare receiver operating characteristic (ROC curves). Full or 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. It is available for R (command-line interface) and S+ (with an additional graphical user interface).

You can find it on ExPASy (thanks Céline from the SIB web-team) and in a few minutes on the CRAN (it has to pass a few checks before it is formally accepted).

But there is no need to download the package. The installation can be done in one command directly from R:

install.packages("pROC")

The package must then be loaded with:

library(pROC)

To get help, enter the following in the R prompt:

?pROC

And that's it! You can have a look at the screenshots to see what it looks like.

Simultaneously we submitted a paper (a short application note) describing it to Bioinformatics. Crossed fingers in the hope it will be accepted!

2010-06-15 follow-up: the paper has been rejected. We will resubmit it to BMC Bioinformatics.

2011-03-17 follow-up: the paper was published in BMC Bioinformatics.