Two Step IPF-Lasso with prespecified penalty factors

The penalty factors get prespecified via ridge regression on the training data. The block 
    structure has to be supplied.

Fraction of all pairs of subjects whose predicted survival times are correctly ordered among all subjects that can actually be ordered. In other words, it is the probability of concordance between the predicted and the observed survival. Corrected by weighting with IPCW as suggested by Uno. Implemented in survAUC::UnoC.

Integrated brier score using Kaplan-Meier estimator for weighting

Only works for methods for which probabilities are provided via pec::predictSurvProb. Currently these are only coxph and randomForestSRC. To set an upper time limit, set argument max.time (defaults to max time in test data). Implemented in pec::pec

Amount of features of feature group 'clin' used for the model

Useful for feature selection. The feature group is specified via extra.arg.
    The names of the features within the group must contain that string. Make sure names of
    features of other groups do not contain this string.

Amount of features of feature group 'cnv' used for the model

Amount of features of feature group 'mirna' used for the model

Amount of features of feature group 'mutation' used for the model

Amount of features of feature group 'rna' used for the model