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: 1.1
Depends: kernlab, MASS, Matrix, foreach, glmnet, R (≥ 2.10)
Published: 2020-04-22
DOI: 10.32614/CRAN.package.DTRlearn2
Author: Yuan Chen, Ying Liu, Donglin Zeng, Yuanjia Wang
Maintainer: Yuan Chen <irene.yuan.chen at gmail.com>
License: GPL-2
NeedsCompilation: no
In views: CausalInference
CRAN checks: DTRlearn2 results


Reference manual: DTRlearn2.pdf


Package source: DTRlearn2_1.1.tar.gz
Windows binaries: r-devel: DTRlearn2_1.1.zip, r-release: DTRlearn2_1.1.zip, r-oldrel: DTRlearn2_1.1.zip
macOS binaries: r-release (arm64): DTRlearn2_1.1.tgz, r-oldrel (arm64): DTRlearn2_1.1.tgz, r-release (x86_64): DTRlearn2_1.1.tgz, r-oldrel (x86_64): DTRlearn2_1.1.tgz
Old sources: DTRlearn2 archive

Reverse dependencies:

Reverse suggests: polle


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