Other packages > Find by keyword >

DynTxRegime  

Methods for Estimating Optimal Dynamic Treatment Regimes
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("DynTxRegime", "4.16")



Attach the package and use:
library("DynTxRegime")
Maintained by
Shannon T. Holloway
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-06-11
Latest Update: 2025-05-03
Description:
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.
How to cite:
Shannon T. Holloway (2015). DynTxRegime: Methods for Estimating Optimal Dynamic Treatment Regimes. R package version 4.16, https://cran.r-project.org/web/packages/DynTxRegime. Accessed 25 Jul. 2026.
Previous versions and publish date:
2.1 (2015-06-11 07:27), 3.0 (2017-05-16 01:32), 3.01 (2017-05-22 01:04), 3.2 (2018-02-12 21:31), 4.0 (2019-02-26 23:50), 4.1 (2019-03-21 14:53), 4.2 (2019-10-17 07:30), 4.3 (2019-12-13 17:40), 4.4 (2020-06-05 20:50), 4.5 (2020-06-27 20:30), 4.6 (2020-07-04 06:30), 4.7 (2020-07-22 06:42), 4.8 (2020-10-16 08:20), 4.9 (2020-11-09 20:10), 4.10 (2022-06-07 14:20), 4.11 (2022-09-29 17:10), 4.12 (2023-04-25 16:50), 4.14 (2023-08-29 15:30), 4.15 (2023-11-24 18:10), (2026-07-09 08:02)
Other packages that cited DynTxRegime R package
View DynTxRegime citation profile
Other R packages that DynTxRegime depends, imports, suggests or enhances
Complete documentation for DynTxRegime
Functions, R codes and Examples using the DynTxRegime R package
Some associated functions: BOWL-class . BOWL-methods . BOWLBasic-class . BOWLBasic-methods . BOWLObj-class . CVBasic-class . CVInfo-class . CVInfo-methods . CVInfo2Par-class . CVInfo2Par-methods . CVInfoLambda-class . CVInfoLambda-methods . CVInfoObj-class . CVInfoObj-methods . CVInfokParam-class . CVInfokParam-methods . Call . ClassificationFit-class . ClassificationFit-methods . ClassificationFit_SubsetList-class . ClassificationFit_SubsetList-methods . ClassificationFit_fSet-class . ClassificationFit_fSet-methods . ClassificationObj-class . ClassificationObj-methods . DTRstep . DecisionPointList-class . DecisionPointList-methods . DynTxRegime-class . DynTxRegime-internal-api . DynTxRegime-methods . EARL-class . EARL-methods . ExpSurrogate-class . ExpSurrogate-methods . HingeSurrogate-class . HingeSurrogate-methods . HuberHingeSurrogate-class . HuberHingeSurrogate-methods . IQLearnFS-class . IQLearnFS-methods . IQLearnFS_C-class . IQLearnFS_C-methods . IQLearnFS_ME-class . IQLearnFS_ME-methods . IQLearnFS_VHet-class . IQLearnFS_VHet-methods . IQLearnSS-class . IQLearnSS-methods . Kernel-class . Kernel-methods . KernelObj-class . KernelObj-methods . Learning-class . Learning-methods . LearningMulti-class . LearningMulti-methods . LearningObject-class . LearningObject-methods . LinearKernel-class . LinearKernel-methods . List-class . LogitSurrogate-class . LogitSurrogate-methods . MethodObject-class . MethodObject-methods . ModelObjSubset-class . ModelObjSubset-methods . ModelObj_DecisionPointList-class . ModelObj_SubsetList-class . MultiRadialKernel-class . MultiRadialKernel-methods . OWL-class . OWL-methods . OptimBasic-class . OptimBasic-methods . OptimKernel-class . OptimKernel-methods . OptimObj-class . OptimObj-methods . OptimStep-class . OptimStep-methods . OptimStep . OptimalClass-class . OptimalClass-methods . OptimalClassObj-class . OptimalInfo-class . OptimalInfo-methods . OptimalObj-class . OptimalObj-methods . OptimalSeq-class . OptimalSeq-methods . OptimalSeqCoarsened-class . OptimalSeqCoarsened-methods . OptimalSeqMissing-class . OptimalSeqMissing-methods . OutcomeIterateFit-class . OutcomeIterateFit-methods . OutcomeNoFit-class . OutcomeNoFit-methods . OutcomeObj-class . OutcomeObj-methods . OutcomeSimpleFit-class . OutcomeSimpleFit-methods . OutcomeSimpleFit_SubsetList-class . OutcomeSimpleFit_SubsetList-methods . OutcomeSimpleFit_fSet-class . OutcomeSimpleFit_fSet-methods . PolyKernel-class . PolyKernel-methods . PropensityFit-class . PropensityFit-methods . PropensityFit_SubsetList-class . PropensityFit_SubsetList-methods . PropensityFit_fSet-class . PropensityFit_fSet-methods . PropensityObj-class . PropensityObj-methods . QLearn-class . QLearnObj-class . RWL-class . RWL-methods . RadialKernel-class . RadialKernel-methods . Regime-class . Regime-methods . RegimeObj-class . RegimeObj-methods . SmoothRampSurrogate-class . SmoothRampSurrogate-methods . SqHingeSurrogate-class . SqHingeSurrogate-methods . SubsetList-class . SubsetList-methods . Surrogate-class . Surrogate-methods . TxInfoBasic-class . TxInfoBasic-methods . TxInfoFactor-class . TxInfoFactor-methods . TxInfoInteger-class . TxInfoInteger-methods . TxInfoList-methods . TxInfoList . TxInfoNoSubsets-class . TxInfoNoSubsets-methods . TxInfoWithSubsets-class . TxInfoWithSubsets-methods . TxObj-class . TxObj-methods . TxSubset-class . TxSubset-methods . TxSubsetFactor-class . TxSubsetFactor-methods . TxSubsetInteger-class . TxSubsetInteger-methods . TypedFit-class . TypedFit-methods . TypedFitObj-class . TypedFitObj-methods . TypedFit_SubsetList-class . TypedFit_SubsetList-methods . TypedFit_fSet-class . TypedFit_fSet-methods . bmiData . bowl . buildModelObjSubset . classif . coef . createearl . createowl . createrwl . cvInfo . cycleList . dot-optimalClass . earl . estimator . fSet . fitObject . fittedCont . fittedMain . genetic . getOutcome . getPrWgt . internal-earl-class . internal-earl-methods . internal-owl-class . internal-owl-methods . internal-rwl-class . internal-rwl-methods . iqLearn . iter . moPropen . newBOWL . newBOWLStep . newCVInfo . newCVInfoObj . newCVStep . newClassificationFit . newClassificationObj . newEARL . newIQLearnFS_C . newIQLearnFS_ME . newIQLearnFS_VHet . newIQLearnSS . newKernelObj . newLearning . newModel . newModelObjSubset . newOWL . newOptim . newOptimObj . newOptimalClass . newOptimalSeq . newOutcomeFit . newOutcomeObj . newPropensityFit . newPropensityObj . newQLearn . newRWL-methods . newRWL . newRegime . newRegimeObj . newTxObj . newTxSubset . newTypedFit . newTypedFitObj . optTx . optimObj . optimalClass . optimalSeq . outcome . owl . plot . propen . qLearn . regimeCoef . residuals . rwl . sd . seqFunc . summary . 
Some associated R codes: A_DecisionPointList.R . A_DynTxRegime.R . A_List.R . A_ModelObjSubset.R . A_ModelObj_DecisionPointList.R . A_ModelObj_SubsetList.R . A_OptimalInfo.R . A_OptimalObj.R . A_SubsetList.R . A_generics.R . A_newModelObjSubset.R . B_TxInfoBasic.R . B_TxInfoFactor.R . B_TxInfoInteger.R . B_TxInfoList.R . B_TxInfoNoSubsets.R . B_TxInfoWithSubsets.R . B_TxObj.R . B_TxSubset.R . B_TxSubsetFactor.R . B_TxSubsetInteger.R . C_TypedFit.R . C_TypedFitObj.R . C_TypedFit_SubsetList.R . C_TypedFit_fSet.R . D_OutcomeIterateFit.R . D_OutcomeNoFit.R . D_OutcomeObj.R . D_OutcomeSimpleFit.R . D_OutcomeSimpleFit_SubsetList.R . D_OutcomeSimpleFit_fSet.R . D_newModel.R . E_class_IQLearnFS.R . E_class_IQLearnFS_C.R . E_class_IQLearnFS_ME.R . E_class_IQLearnFS_VHet.R . E_class_IQLearnSS.R . E_class_QLearn.R . E_iqLearnFSC.R . E_iqLearnFSM.R . E_iqLearnFSV.R . E_iqLearnSS.R . E_qLearn.R . F_PropensityFit.R . F_PropensityFit_SubsetList.R . F_PropensityFit_fSet.R . F_PropensityObj.R . G_Regime.R . G_RegimeObj.R . H_class_OptimalSeq.R . H_class_OptimalSeqCoarsened.R . H_class_OptimalSeqMissing.R . H_optimalSeq.R . I_ClassificationFit.R . I_ClassificationFit_SubsetList.R . I_ClassificationFit_fSet.R . I_ClassificationObj.R . J_class_OptimalClass.R . J_optimalClass.R . K_Kernel.R . K_KernelObj.R . K_LinearKernel.R . K_MultiRadialKernel.R . K_PolyKernel.R . K_RadialKernel.R . L_ExpSurrogate.R . L_HingeSurrogate.R . L_HuberHingeSurrogate.R . L_LogitSurrogate.R . L_SmoothRampSurrogate.R . L_SqHingeSurrogate.R . L_Surrogate.R . M_MethodObject.R . M_OptimBasic.R . M_OptimKernel.R . M_OptimObj.R . N_CVBasic.R . N_CVInfo.R . N_CVInfo2Par.R . N_CVInfoLambda.R . N_CVInfoObj.R . N_CVInfokParam.R . N_OptimStep.R . O_Learning.R . O_LearningMulti.R . O_LearningObject.R . P_class_.owl.R . P_class_OWL.R . P_owl.R . Q_class_.rwl.R . Q_class_RWL.R . Q_rwl.R . R_bowl.R . R_class_BOWL.R . R_class_BOWLBasic.R . S_class_.earl.R . S_class_EARL.R . S_earl.R . checkFSetAndOutcomeModels.R . checkFSetAndPropensityModels.R . checkInputs.R . internalTest.R . titleIt.R .  Full DynTxRegime package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

dendroextras  
Extra Functions to Cut, Label and Colour Dendrogram Clusters
Provides extra functions to manipulate dendrograms that build on the base functions provided by the ...
Download / Learn more Package Citations See dependency  
lomb  
Lomb-Scargle Periodogram
Computes the Lomb-Scargle Periodogram and actogram for evenly or unevenly sampled time series. Inclu ...
Download / Learn more Package Citations See dependency  
UnitStat  
Performs Unit Root Test Statistics
A test to understand the stability of the underlying stochastic data. Helps the user’s understand ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  

27,987

R Packages

239,283

Dependencies

74,234

Author Associations

27,988

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA