DTRlearn2
1.1Statistical Learning Methods for Optimizing Dynamic Treatment Regimes
Overview
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.
Install
Health
- NOTE r-devel-linux-x86_64-debian-clang
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 50%
Downloads
Dependencies
Code & Tests
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People & History
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2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 1.1Latest
- RR 3.6.0 released · 2019-04-26
- 1.02019-01-03
- RR 3.5.0 released · 2018-04-23
Package metadata
- First published
- 2019-01-03
- Total releases
- 2 / 7 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 2.5 KB / 1 file
- Download size
- 19 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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