Package: DTRlearn2 2.0
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.
Authors:
DTRlearn2_2.0.tar.gz
DTRlearn2_2.0.zip(r-4.5)DTRlearn2_2.0.zip(r-4.4)DTRlearn2_2.0.zip(r-4.3)
DTRlearn2_2.0.tgz(r-4.4-any)DTRlearn2_2.0.tgz(r-4.3-any)
DTRlearn2_2.0.tar.gz(r-4.5-noble)DTRlearn2_2.0.tar.gz(r-4.4-noble)
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DTRlearn2.pdf |DTRlearn2.html✨
DTRlearn2/json (API)
# Install 'DTRlearn2' in R: |
install.packages('DTRlearn2', repos = c('https://ychen178.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ychen178/dtrlearn2/issues
- adhd - A 2-stage SMART data of children with ADHD
Last updated 2 years agofrom:5a873a1484. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 16 2024 |
R-4.5-win | NOTE | Oct 16 2024 |
R-4.5-linux | NOTE | Oct 16 2024 |
R-4.4-win | NOTE | Oct 16 2024 |
R-4.4-mac | NOTE | Oct 16 2024 |
R-4.3-win | NOTE | Oct 16 2024 |
R-4.3-mac | NOTE | Oct 16 2024 |
Exports:owlpredict.owlpredict.qlqlsim_Kstage
Dependencies:codetoolsforeachglmnetiteratorskernlablatticeMASSMatrixRcppRcppEigenshapesurvivalWeightSVM