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DTRlearn2

Statistical Learning Methods for Optimizing Dynamic Treatment Regimes

v1.1 · Apr 22, 2020 · GPL-2

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.

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CRAN incoming feasibility

Maintainer: ‘Yuan Chen <irene.yuan.chen@gmail.com>’

No Authors@R field in DESCRIPTION.
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  Authors@R: c(person(given = "Yuan",
                      family = "Chen",
                      role = c("aut", "cre"),
                      email = "irene.yuan.chen@gmail.com"),
               person(given = "Ying",
                      family = "Liu",
                      role = "aut"),
               person(given = "Donglin",
                      family = "Zeng",
                      role = "aut"),
               person(given = "Yuanjia",
                      family = "Wang",
                      role = "aut"))
as necessary.
NOTE r-devel-linux-x86_64-debian-gcc

CRAN incoming feasibility

Maintainer: ‘Yuan Chen <irene.yuan.chen@gmail.com>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Yuan",
                      family = "Chen",
                      role = c("aut", "cre"),
                      email = "irene.yuan.chen@gmail.com"),
               person(given = "Ying",
                      family = "Liu",
                      role = "aut"),
               person(given = "Donglin",
                      family = "Zeng",
                      role = "aut"),
               person(given = "Yuanjia",
                      family = "Wang",
                      role = "aut"))
as necessary.
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NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-devel-linux-x86_64-debian-clang

CRAN incoming feasibility

Maintainer: ‘Yuan Chen <irene.yuan.chen@gmail.com>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Yuan",
                      family = "Chen",
                      role = c("aut", "cre"),
                      email = "irene.yuan.chen@gmail.com"),
               person(given = "Ying",
                      family = "Liu",
                      role = "aut"),
               person(given = "Donglin",
                      family = "Zeng",
          
NOTE r-devel-linux-x86_64-debian-gcc

CRAN incoming feasibility

Maintainer: ‘Yuan Chen <irene.yuan.chen@gmail.com>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Yuan",
                      family = "Chen",
                      role = c("aut", "cre"),
                      email = "irene.yuan.chen@gmail.com"),
               person(given = "Ying",
                      family = "Liu",
                      role = "aut"),
               person(given = "Donglin",
                      family = "Zeng",
          

Reverse Dependencies (1)

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Dependency Network

Dependencies Reverse dependencies kernlab MASS Matrix foreach glmnet polle DTRlearn2

Version History

new 1.1 Mar 9, 2026