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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 |
| 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 suggests: | polle |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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