Methods for Estimating Optimal Dynamic Treatment Regimes

Methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


4.9 by Shannon T. Holloway, 10 months ago

Browse source code at

Authors: S. T. Holloway , E. B. Laber , K. A. Linn , B. Zhang , M. Davidian , and A. A. Tsiatis

Documentation:   PDF Manual  

GPL-2 license

Imports kernlab, rgenoud, dfoptim

Depends on methods, modelObj, stats

Suggests MASS, rpart, nnet

Imported by DevTreatRules.

See at CRAN