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.
| Version: |
2.1 |
| Depends: |
R (≥ 2.10) |
| Imports: |
kernlab, MASS, Matrix, foreach, glmnet, WeightSVM |
| Published: |
2026-09-10 |
| DOI: |
10.32614/CRAN.package.DTRlearn2 |
| Author: |
Yuan Chen [aut, cre],
Ying Liu [aut],
Tianchen Xu  [ctb],
Donglin Zeng [ctb],
Yuanjia Wang [ctb] |
| Maintainer: |
Yuan Chen <irene.yuan.chen at gmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Materials: |
README |
| In views: |
CausalInference |
| CRAN checks: |
DTRlearn2 results |
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