Package: DynTxRegime 4.15

DynTxRegime: 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.

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

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DynTxRegime/json (API)
NEWS

# Install 'DynTxRegime' in R:
install.packages('DynTxRegime', repos = c('https://sth1402.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

32 exports 2 stars 1.51 score 4 dependencies 2 dependents 2 mentions 106 scripts 534 downloads

Last updated 10 months agofrom:8b2d0558cf. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 21 2024
R-4.5-winOKAug 21 2024
R-4.5-linuxOKAug 21 2024
R-4.4-winOKAug 21 2024
R-4.4-macOKAug 21 2024
R-4.3-winOKAug 21 2024
R-4.3-macOKAug 21 2024

Exports:bowlbuildModelObjSubsetCallclassifcoefcvInfoDTRstepearlestimatorfitObjectgeneticiqLearnFSCiqLearnFSMiqLearnFSViqLearnSSoptimalClassoptimalSeqoptimObjoptTxoutcomeowlplotprintpropenqLearnqqplotregimeCoefresidualsrwlsdshowsummary

Dependencies:dfoptimkernlabmodelObjrgenoud

Readme and manuals

Help Manual

Help pageTopics
Adolescent BMI dataset (generated toy example)bmiData
Backwards Outcome Weighted Learning.bowl
Create Model Objects for Subsets of DatabuildModelObjSubset
Retrieve Unevaluated Original CallCall
Retrieve Classification Regression Analysisclassif classif,OptimalClass-method
Extract Model Coefficients From Objects Returned by Modeling Functionscoef
Extract Cross-Validation ResultscvInfo
Identify Statistical Method Used to Obtain ResultDTRstep
Efficient Augmentation and Relaxation Learningearl
Class 'EARL'EARL-class
Retrieve the Estimated Valueestimator estimator,IQLearnFS-method estimator,IQLearnSS-method
Objects Returned by Modeling FunctionsfitObject
Retrieve the Fitted Contrast Component from Second Stage IQ-LearningfittedCont fittedCont,IQLearnSS-method
Retrieve the Fitted Main Effects Component from Second Stage IQ-LearningfittedMain fittedMain,IQLearnSS-method
Defining the fSet Input VariablefSet
Retrieve the Genetic Algorithm Resultsgenetic genetic,OptimalSeq-method
Interactive Q-LearningiqLearn iqLearnFSC iqLearnFSM iqLearnFSV iqLearnSS
Class 'IQLearnFS_C'IQLearnFS_C-class
Class 'IQLearnFS_ME'IQLearnFS_ME-class
Class 'IQLearnFS_VHet'IQLearnFS_VHet-class
Class 'IQLearnSS'IQLearnSS-class
Defining the iter Input Variableiter
Defining the moPropen Input VariablemoPropen
Classification PerspectiveoptimalClass
Class 'OptimalClass'OptimalClass-class
Class 'OptimalClassObj'OptimalClassObj-class
Class 'OptimalInfo'OptimalInfo-class
Missing or Coarsened Data Perspective - Genetic AlgorithmoptimalSeq
Class 'OptimalSeq'OptimalSeq-class
Class Contains Results for the Coarsened Data IPW/AIPW MethodOptimalSeqCoarsened-class
Class Contains Results for the Missing Data IPW/AIPW MethodOptimalSeqMissing-class
Extract Optimization ResultsoptimObj
Extract or Estimate the Optimal Tx and Decision FunctionsoptTx optTx,IQLearnFS,data.frame-method optTx,IQLearnFS,missing-method
Retrieve Outcome Regression Analysisoutcome
Outcome Weighted Learningowl
Class 'OWL'OWL-class
Generates Plots as Defined by Modeling Functionsplot
Retrieve Propensity Regression Analysispropen
A Step of the Q-Learning AlgorithmqLearn
Class 'QLearn'QLearn-class
Class 'QLearnObj'QLearnObj-class
Extract Regime ParametersregimeCoef
Extract Model Residualsresiduals residuals,IQLearnFS_C-method residuals,IQLearnFS_VHet-method
Residual Weighted Learningrwl
Class 'RWL'RWL-class
Standard Deviationsd sd,IQLearnFS_C-method
Result Summariessummary