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.
Package details |
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Author | Yuan Chen, Ying Liu, Donglin Zeng, Yuanjia Wang |
Maintainer | Yuan Chen <irene.yuan.chen@gmail.com> |
License | GPL-2 |
Version | 1.1 |
Package repository | View on CRAN |
Installation |
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