DTRlearn: Learning Algorithms for Dynamic Treatment Regimes

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Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by time-varying subject-specific features and intermediate outcomes observed in previous stages. This package implements three methods: O-learning (Zhao et. al. 2012,2014), Q-learning (Murphy et. al. 2007; Zhao et.al. 2009) and P-learning (Liu et. al. 2014, 2015) to estimate the optimal DTRs.

Author
Ying Liu, Yuanjia Wang, Donglin Zeng
Date of publication
2015-12-28 00:06:00
Maintainer
Ying Liu <yl2802@cumc.columbia.edu>
License
GPL-2
Version
1.2

View on CRAN

Man pages

DTRlearn-package
Dynamic Treatment Regimens Learning
make_2classification
Data Simulation for 2 stages
make_classification
Data Simulation for single stage
Olearning
Multiple stage Improved Olearning
Olearning_Single
Improved single stage O-learning with cross validation
Plearning
Plearning
plot.linearcl
Plot coefficients for SVM with linear kernel
plot.qlearn
Plot the linear coefficients of interaction
predict.linearcl
Predict
predict.qlearn
Predict optimal treatment by Qlearning
predict.rbfcl
Predict
Qlearning
Q-learning
Qlearning_Single
Single Stage Q learning
wsvm
weighted SVM

Files in this package

DTRlearn
DTRlearn/NAMESPACE
DTRlearn/R
DTRlearn/R/Qlearning.R
DTRlearn/R/Plearning.R
DTRlearn/R/plot.R
DTRlearn/R/predict.R
DTRlearn/R/wSVM.R
DTRlearn/R/Olearning.R
DTRlearn/R/make_classification.R
DTRlearn/MD5
DTRlearn/DESCRIPTION
DTRlearn/man
DTRlearn/man/make_2classification.Rd
DTRlearn/man/Olearning.Rd
DTRlearn/man/DTRlearn-package.Rd
DTRlearn/man/Olearning_Single.Rd
DTRlearn/man/plot.qlearn.Rd
DTRlearn/man/make_classification.Rd
DTRlearn/man/plot.linearcl.Rd
DTRlearn/man/Qlearning.Rd
DTRlearn/man/Plearning.Rd
DTRlearn/man/Qlearning_Single.Rd
DTRlearn/man/wsvm.Rd
DTRlearn/man/predict.qlearn.Rd
DTRlearn/man/predict.rbfcl.Rd
DTRlearn/man/predict.linearcl.Rd