**Integrative Lasso with Penalty Factors** (**ipflasso**)

The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen by cross-validation.

**Predicting Rankings of Labels** (**labelrank**)

An implementation of distance-based ranking algorithms to predict rankings of labels. Two common algorithms are included: the naive Bayes and the nearest neighbor algorithms.

**Bayesian Semiparametric Models for Interval-Censored Data** (**ICBayes**)

Contains functions to fit Bayesian semiparametric regression survival models (proportional hazards model, proportional odds model, and probit model) to interval-censored time-to-event data.

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