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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1 |
rolling linux/jammy R-4.5 | DTRlearn2_1.1.tar.gz |
122.7 KiB |
1.1 |
rolling linux/noble R-4.5 | DTRlearn2_1.1.tar.gz |
122.7 KiB |
1.1 |
rolling source/ R- | DTRlearn2_1.1.tar.gz |
19.4 KiB |
1.1 |
latest linux/jammy R-4.5 | DTRlearn2_1.1.tar.gz |
122.7 KiB |
1.1 |
latest linux/noble R-4.5 | DTRlearn2_1.1.tar.gz |
122.7 KiB |
1.1 |
latest source/ R- | DTRlearn2_1.1.tar.gz |
19.4 KiB |
1.1 |
2026-04-26 source/ R- | DTRlearn2_1.1.tar.gz |
19.4 KiB |
1.1 |
2026-04-23 source/ R- | DTRlearn2_1.1.tar.gz |
19.4 KiB |
1.1 |
2026-04-09 windows/windows R-4.5 | DTRlearn2_1.1.zip |
125.5 KiB |
1.1 |
2025-04-20 source/ R- | DTRlearn2_1.1.tar.gz |
19.4 KiB |