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:Yuan Chen, Ying Liu, Donglin Zeng, Yuanjia Wang

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)
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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'))

Peer review:

Bug tracker:https://github.com/ychen178/dtrlearn2/issues

Datasets:
  • adhd - A 2-stage SMART data of children with ADHD

On CRAN:

3.54 score 7 stars 6 scripts 268 downloads 5 exports 13 dependencies

Last updated 2 years agofrom:5a873a1484. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 15 2024
R-4.5-winOKNov 15 2024
R-4.5-linuxOKNov 15 2024
R-4.4-winNOTENov 15 2024
R-4.4-macNOTENov 15 2024
R-4.3-winNOTENov 15 2024
R-4.3-macNOTENov 15 2024

Exports:owlpredict.owlpredict.qlqlsim_Kstage

Dependencies:codetoolsforeachglmnetiteratorskernlablatticeMASSMatrixRcppRcppEigenshapesurvivalWeightSVM