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DTRlearn2  

Statistical Learning Methods for Optimizing Dynamic Treatment Regimes
View on CRAN: Click here


Download and install DTRlearn2 package within the R console
Install from CRAN:
install.packages("DTRlearn2")

Install from Github:
library("remotes")
install_github("cran/DTRlearn2")

Install by package version:
library("remotes")
install_version("DTRlearn2", "1.1")



Attach the package and use:
library("DTRlearn2")
Maintained by
Yuan Chen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-01-03
Latest Update: 2020-04-22
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.
How to cite:
Yuan Chen (2019). DTRlearn2: Statistical Learning Methods for Optimizing Dynamic Treatment Regimes. R package version 1.1, https://cran.r-project.org/web/packages/DTRlearn2. Accessed 15 Jul. 2026.
Previous versions and publish date:
1.0 (2019-01-03 18:00), (2026-07-09 08:01)
Other packages that cited DTRlearn2 R package
View DTRlearn2 citation profile
Other R packages that DTRlearn2 depends, imports, suggests or enhances
Complete documentation for DTRlearn2
Functions, R codes and Examples using the DTRlearn2 R package
Some associated functions: adhd . owl . predict.owl . predict.ql . ql . sim_Kstage . 
Some associated R codes: owl.R . owl_aug.R . owl_l2.R . owl_logit.R . owl_ramp.R . predict_all.R . ql.R . sim_Kstage.R . wsvm_solve.R .  Full DTRlearn2 package functions and examples
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