CRAN
DTRlearn2 1.0
Statistical Learning Methods for Optimizing Dynamic Treatment Regimes
Released Jan 3, 2019 by Yuan Chen
Dependencies
MASS 7.3-51.1 Matrix 1.2-15 foreach 1.4.4 kernlab 0.9-27 glmnet 2.0-16
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.
Installation
Maven
This package can be included as a dependency from a Java or Scala project by including
the following your project's pom.xml file.
Read more
about embedding Renjin in JVM-based projects.
<dependencies>
<dependency>
<groupId>org.renjin.cran</groupId>
<artifactId>DTRlearn2</artifactId>
<version>1.0-b1</version>
</dependency>
</dependencies>
<repositories>
<repository>
<id>bedatadriven</id>
<name>bedatadriven public repo</name>
<url>https://nexus.bedatadriven.com/content/groups/public/</url>
</repository>
</repositories>
Renjin CLI
If you're using Renjin from the command line, you load this library by invoking:
library('org.renjin.cran:DTRlearn2')
Test Results
This package was last tested against Renjin 0.9.2718 on Jan 5, 2019.