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DynTxRegime (version 3.01)

Methods for Estimating Optimal Dynamic Treatment Regimes

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.

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Version

Install

install.packages('DynTxRegime')

Monthly Downloads

483

Version

3.01

License

GPL-2

Maintainer

Shannon Holloway

Last Published

May 21st, 2017

Functions in DynTxRegime (3.01)

BOWLObj-class

Class
BOWLWithOneRegime-class

Class
BOWLWithSubsetRegimes-class

Class
CVInfo-class

Class
CVInfo1Par-class

Class
CVInfo2Par-class

Class
BOWL-class

Class
bowl

BOWLBasic_fSet-class

Class
BOWLBasic-class

Class
DecisionPointList-class

Class
DynTxRegime-class

Class
IQLearnFS_ME-class

Class
IQLearnFS_VHet-class

Class
OWLOptim-class

Class
OptimBasic-class

Class
OptimalClassAIPWE-class

Class
OptimalClassIPWE-class

Class
internals

~~
DynTxRegime-package

IterateFitWithSubsets-class

Class
OptimKernel-class

Class
OptimalClass-class

Class
OptimalSeq-class

Class
OptimalSeqAIPWE_MDP-class

Class
EARL-class

Class
earl

IQLearnSS-class

Class
IterateFit-class

Class
IterateFitNoSubsets-class

Class
PropensityFit_SubsetList_DecisionPointList-class

Class
PropensityFit_fSet-class

Class
TxInfoList-class

Class
TxInfoNoSubsets-class

Class
EARLAIPWE-class

Class
EARLIPWE-class

Class
ModelObj_SubsetList_DecisionPointList-class

Class
List-class

Class
ModelObjSubset-class

Class
ModelObj_DecisionPointList-class

Class
ModelObj_SubsetList-class

Class
MultipleDecisionPoint-class

Class
PropensityOnly-class

Class
PropensityRegression-class

Class
Regime_DecisionPointList-class

Class
Regime-class

Class
RegimeObject-class

Class
iter

moPropen

.newBOWLOptimization

Backward Outcome Weighted Learning
.newCVInfo

Create Cross-Validation Objects
.newPropensityRegression

Complete Propensity Score Regression Step.
.newQLearn

A Step of the Q-Learning Algorithm
EARLOptim-class

Class
IQLearnBase-class

Class
IQLearnFS-class

Class
OptimalSeqIPWE_SDP-class

Class
OutcomeOnly-class

Class
QLearn-class

Class
RWL-class

Class
coef

Retrieve Model Coefficients
cvInfo

Retrieve Cross-Validation Results
genetic

Retrieve the Result of the Genetic Algorithm Optimization
optTx

Methods to Retrieve Estimated Optimal Treatment
buildModelObjSubset

fSet

.newIQLearnFS_ME

Third Step of IQ-Learning Algorithm
.newIQLearnFS_VHet

Fourth Step of IQ-Learning Algorithm
IQLearnFS_C-class

Class
OWL-class

Class
owl

OutcomeRegression-class

Class
SubsetListFit_DecisionPointList-class

Class
SubsetsModeled-class

Class
TxInfoFactor-class

Class
TxInfoFactorWithSubsets-class

Class
OutcomeRegression_DecisionPointList-class

Class
PropensityAndOutcome-class

Class
PropensityFit-class

Class
rwl

RWLOptim-class

Class
TxInfoWithSubsets-class

Class
TxSubset-class

Class
SingleDecisionPoint-class

Class
SubsetsNotModeled-class

Class
TxInfoBasic-class

Class
TypedSimpleFit-class

Class
.newBOWL

Backward Outcome Weighted Learning.
.newBOWLBasic

Backward Outcome Weighted Learning With Binary Treatment.
.newModelObjSubset

Create an object of class
.newOWL

Outcome Weighted Learning.
.newEARL

Efficient Augmentation and Relaxation Learning.
.newIQLearnFS_C

Second Step of IQ-Learning Algorithm
.newRWL

Residual Weighted Learning.
.newRegime

Process and Store Decision Rule Information.
iqLearnFSM

IQ-Learning: Regression of Estimated Second-Stage Main Effects
iqLearnFSC

IQ-Learning: Regression of Estimated Second-Stage Contrasts
estimator

Estimated Value of Estimated Optimal Regime
fitObject

Modeling Function Value Objects
.newOptimalSeq

Value Search Method for Optimal Treatment Regime.
.newOutcomeRegression

Complete Outcome Regression Step When Subset Modeling
sd

Standard Deviation of IQ-Learning Variance Step
summary

Summary Results
OptimalSeqAIPWE_SDP-class

Class
OptimalSeqIPWE_MDP-class

Class
PropensityFit_DecisionPointList-class

Class
PropensityFit_SubsetList-class

Class
TypedSimpleFitNoSubsets-class

Class
.newIQLearnSS

First Step of IQ-Learning Algorithm
.newIterateFit

Complete Outcome Regression Step When Two Component Model.
DTRstep

Identify Statistical Method of DynTxRegime Object
optimalClass

optimalSeq

.newOWLOptim

Optimization Routine - Outcome Weighted Learning.
.newOptimalClass

Optimal Treatment Regime from Classification Perspective.
.newTxInfoWithSubsets

Create Treatment Information Object When Subsets are Identified
classif

Retrieve Classification Value Object
fittedCont

Extract Fitted Contrast Component of Outcome
fittedMain

Extract Fitted Main Effects of Outcome
plugInValue

Estimate Plug-in Value
SubsetListFit-class

Class
TxInfoInteger-class

Class
TxInfoIntegerWithSubsets-class

Class
.newTypedSimpleFit

Complete Outcome Regression Step When Single Model.
plot

Generate Standard Plots
propen

Retrieve Regression Objects for Propensity Score
residuals

Extract Model Residuals
show

Show an Object
SubsetList-class

Class
qLearn

Q-learning
TypedSimpleFitWithSubsets-class

Class
bmiData

.newTxInfo

Create Treatment Information Objects
.newTxInfoNoSubsets

Create Treatment Information Object When Subsets are not Identified
iqLearnSS

IQ-Learning: Second-Stage Regression
iqLearnFSV

IQ-Learning: Variance of the Regression of the Estimated Second-Stage Contrast (IQ3)
optimObj

Retrieve Optimization Value Object
outcome

Retrieve Regression Objects for the Outcome Regression Models
qqplot

IQ-Learning: Generate QQ-Plots for Variance Modeling .
regimeCoef

Retrieve Regime Parameter Estimates