After nearly two years since the previous release, pROC 1.9.1 is finally available on CRAN. Here is a list of the main changes:

• subset and na.action arguments now handled properly in roc.formula. This means you can now do something like this:
  

  data(aSAH)
  roc(outcome ~ s100b, data=aSAH, subset=(gender == "Male"))
  roc(outcome ~ s100b, data=aSAH, subset=(gender == "Female"))

  Thanks Terry Therneau for the report.
• Added policies to handle the case where a ROC curve has multiple "best" threshold in ci.coords. The following policies are available:
  • "stop" will abort the processing and throw an error (with stop). This is the default.
  • "omit" will ignore the sample (as in NULL). This can lead to a reduced effective number of usable sample in the final statistic.
  • "random" will select one of the threshold randomly.
  Here is an example of usage:
  

  data(aSAH)
  ci.coords(aSAH$outcome, aSAH$s100b, x="best", input = "threshold", 
            ret=c("specificity", "ppv", "tp"),
            best.policy = "random")

  Thanks Nicola Toschi for the report.
• Support xlim and ylim gracefully in plot.roc.
• Improved validation of input class levels and direction; A message can be printed when auto-detecting, use the quiet argument to turn on.
• Removed extraneous name attribute on the p.value (thanks Paweł Kleka for the report).
• Faster DeLong algorithm (code contributed by Stefan Siegert). The code is based on the algorithm by Xu Sun and Weichao Xu (2014) that has an O(N log N) complexity instead of O(N²).
  The DeLong algorithm is now always faster than bootstrapping, even in the previous edge case of ROC curve with large number of samples and few thresholds where bootstrapping used to be faster. Here is a quick example with 200000 data points:

  library(pROC)
  n <- 200000
  a <- as.numeric(cut(rnorm(n), c(-Inf, -1, 0, 1, Inf)))
  b <- round(runif(n))
  r <- roc(b, a, algorithm = 3)
  
  # With Bootstrap
  > system.time(var(r, method = "b", progress = "none"))
  utilisateur     système      écoulé 
       25.896       0.136      26.027 
  
  # With old DeLong algorithm
  > system.time(var(r, method = "d"))
  utilisateur     système      écoulé 
       47.352       0.008      47.353 
  
  # With new DeLong algorithm
  > system.time(var(r, method = "d"))
  utilisateur     système      écoulé 
        0.016       0.008       0.023 

Obtaining the update

To update your installation, simply type:

install.packages("pROC")

References

Xu Sun and Weichao Xu (2014) "Fast Implementation of DeLongs Algorithm for Comparing the Areas Under Correlated Receiver Operating Characteristic Curves". IEEE Signal Processing Letters, 21, 1389-1393. DOI: 10.1109/LSP.2014.2337313.