Package: DynTxRegime
Type: Package
Title: Methods for Estimating Optimal Dynamic Treatment Regimes
Version: 4.15
Date: 2023-11-24
Author: S. T. Holloway, E. B. Laber, K. A. Linn, B. Zhang, M. Davidian, and A. A. Tsiatis
Maintainer: Shannon T. Holloway <shannon.t.holloway@gmail.com>
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.
License: GPL-2
Depends: methods, modelObj, stats
Suggests: MASS, rpart, nnet
Imports: kernlab, rgenoud, dfoptim
NeedsCompilation: no
Repository: CRAN
Encoding: UTF-8
RoxygenNote: 7.2.1
Collate: 'A_generics.R' 'A_List.R' 'A_DecisionPointList.R'
        'A_OptimalInfo.R' 'A_OptimalObj.R' 'A_DynTxRegime.R'
        'A_ModelObjSubset.R' 'A_SubsetList.R' 'A_ModelObj_SubsetList.R'
        'A_ModelObj_DecisionPointList.R' 'A_newModelObjSubset.R'
        'B_TxInfoBasic.R' 'B_TxInfoFactor.R' 'B_TxInfoInteger.R'
        'B_TxObj.R' 'B_TxInfoNoSubsets.R' 'B_TxSubset.R'
        'B_TxSubsetInteger.R' 'B_TxSubsetFactor.R'
        'B_TxInfoWithSubsets.R' 'B_TxInfoList.R' 'C_TypedFit.R'
        'C_TypedFit_SubsetList.R' 'C_TypedFit_fSet.R' 'C_TypedFitObj.R'
        'D_OutcomeNoFit.R' 'D_newModel.R' 'D_OutcomeSimpleFit.R'
        'D_OutcomeSimpleFit_fSet.R' 'D_OutcomeIterateFit.R'
        'D_OutcomeSimpleFit_SubsetList.R' 'D_OutcomeObj.R'
        'E_class_QLearn.R' 'E_class_IQLearnSS.R' 'E_class_IQLearnFS.R'
        'E_class_IQLearnFS_C.R' 'E_class_IQLearnFS_ME.R'
        'E_class_IQLearnFS_VHet.R' 'E_iqLearnFSC.R' 'E_iqLearnFSM.R'
        'E_iqLearnFSV.R' 'E_iqLearnSS.R' 'E_qLearn.R'
        'F_PropensityFit.R' 'F_PropensityFit_fSet.R'
        'F_PropensityFit_SubsetList.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_MultiRadialKernel.R'
        'K_RadialKernel.R' 'K_PolyKernel.R' 'K_LinearKernel.R'
        'K_KernelObj.R' 'L_Surrogate.R' 'L_ExpSurrogate.R'
        'L_HingeSurrogate.R' 'L_HuberHingeSurrogate.R'
        'L_LogitSurrogate.R' 'L_SmoothRampSurrogate.R'
        'L_SqHingeSurrogate.R' 'M_MethodObject.R' 'M_OptimBasic.R'
        'M_OptimKernel.R' 'M_OptimObj.R' 'N_CVBasic.R' 'N_CVInfo.R'
        'N_CVInfoLambda.R' 'N_CVInfokParam.R' 'N_CVInfo2Par.R'
        'N_CVInfoObj.R' 'N_OptimStep.R' 'O_LearningObject.R'
        'O_Learning.R' 'O_LearningMulti.R' 'P_class_.owl.R'
        'P_class_OWL.R' 'P_owl.R' 'Q_class_.rwl.R' 'Q_class_RWL.R'
        'Q_rwl.R' 'R_class_BOWLBasic.R' 'R_class_BOWL.R' 'R_bowl.R'
        'S_class_.earl.R' 'S_class_EARL.R' 'S_earl.R'
        'checkFSetAndOutcomeModels.R' 'checkFSetAndPropensityModels.R'
        'checkInputs.R' 'internalTest.R' 'titleIt.R'
Packaged: 2023-11-24 16:38:56 UTC; 19194
Date/Publication: 2023-11-24 17:10:05 UTC
Built: R 4.4.0; ; 2024-04-07 23:21:17 UTC; unix
