Package: DTRlearn2 2.1

DTRlearn2: Statistical Learning Methods for Optimizing Dynamic Treatment Regimes

We provide a comprehensive software to estimate general K-stage DTRs from SMARTs with Q-learning and a variety of outcome-weighted learning methods. Penalizations are allowed for variable selection and model regularization. With the outcome-weighted learning scheme, different loss functions - SVM hinge loss, SVM ramp loss, binomial deviance loss, and L2 loss - are adopted to solve the weighted classification problem at each stage; augmentation in the outcomes is allowed to improve efficiency. The estimated DTR can be easily applied to a new sample for individualized treatment recommendations or DTR evaluation.

Authors:Yuan Chen [aut, cre], Ying Liu [aut], Tianchen Xu [ctb], Donglin Zeng [ctb], Yuanjia Wang [ctb]