Package: DTRlearn2
Title: Statistical Learning Methods for Optimizing Dynamic Treatment
        Regimes
Version: 2.1
Authors@R: c(
    person("Yuan", "Chen", email= "irene.yuan.chen@gmail.com", role=c("aut", "cre")),
    person("Ying", "Liu", role=c("aut")),
    person("Tianchen", "Xu", role = c("ctb"), comment=c(ORCID="0000-0002-0102-7630")),
    person("Donglin", "Zeng", role = c("ctb")),
    person("Yuanjia", "Wang", role = c("ctb")))
Description: 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.
Depends: R(>= 2.10)
Imports: kernlab, MASS, Matrix, foreach, glmnet, WeightSVM
License: GPL-2
Encoding: UTF-8
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2026-09-10 20:32:51 UTC; ychen
Author: Yuan Chen [aut, cre],
  Ying Liu [aut],
  Tianchen Xu [ctb] (ORCID: <https://orcid.org/0000-0002-0102-7630>),
  Donglin Zeng [ctb],
  Yuanjia Wang [ctb]
Maintainer: Yuan Chen <irene.yuan.chen@gmail.com>
Repository: CRAN
Date/Publication: 2026-09-10 20:50:02 UTC
Built: R 4.7.0; ; 2026-09-10 23:52:01 UTC; windows
