Man pages for achambaz/tsml.cara.rct
Targeted Sequential Minimum Loss CARA RCT Design and Inference

addNewSample.TSMLCARAAdds the Newly Sampled Observations
as.character.TSMLCARAReturns a Description of a TSMLCARA Object
getHistory.TSMLCARARetrieves the History of a TSMLCARA Object
getObs.TSMLCARARetrieves the Current Data Set
getOptTmComputes the Optimal Treatment Mechanism Within a Parametric...
getOptVarComputes the Optimal Variance Given a Parametric Model When...
getPsiSd.TSMLCARAReturns the Current Estimated Standard Deviation of the...
getPsi.TSMLCARAReturns the Current Estimator
getRegretSd.TSMLCARAReturns the Current Estimated Standard Deviation of the...
getRegret.TSMLCARAReturns the Current Estimator of the Empirical Regret
getSampleGenerates Data
makeLearnQBuilds a Parametric Working Model Based on Sample Size
makeLearnQ.piecewiseBuilds a Parametric Model Based on Sample Size
oneOneBalanced Treatment Mechanism
plot.TSMLCARAPlots a TSMLCARA Object
printHistoryPrints a Summary of an History of a TSMLCARA Object
Qbar1A Conditional Expectation of Y Given (A,W)
Qbar2A conditional Expectation of Y Given (A,W)
ruleQbarComputes the Treatment Rule Associated with Qbar
setConfLevel.TSMLCARASets a Confidence Level
targetPsi.TSMLCARATargets a TSMLCARA Object Toward the Parameter Psi
tsml.cara.rctTargeted Minimum Loss Covariate-Adjusted Response-Adaptive...
update.TSMLCARAUpdates a TSMLCARA Object
Vbar1A Conditional Variance of Y Given (A,W)
Vbar2A Conditional Variance of Y Given (A,W)
achambaz/tsml.cara.rct documentation built on June 3, 2017, 12:48 p.m.