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

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install.packages("DynTxRegime")

4.16 by Shannon T. Holloway, a year ago


Browse source code at https://github.com/cran/DynTxRegime


Authors: Shannon T. Holloway [aut, cre] , E. B. Laber [aut] , K. A. Linn [aut] , B. Zhang [aut] , M. Davidian [aut] , A. A. Tsiatis [aut]


Documentation:   PDF Manual  


GPL-2 license


Imports kernlab, rgenoud, dfoptim

Depends on methods, modelObj, stats

Suggests MASS, rpart, nnet


Imported by DevTreatRules, causal.decomp, polle.


See at CRAN