R/RcppExports.R

In rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

getFisherCombinationSizeCpp <- function(kMax, alpha0Vec, criticalValues, tVec, cases) {
    .Call(`_rpact_getFisherCombinationSizeCpp`, kMax, alpha0Vec, criticalValues, tVec, cases)
}

getSimulatedAlphaCpp <- function(kMax, alpha0, criticalValues, tVec, iterations) {
    .Call(`_rpact_getSimulatedAlphaCpp`, kMax, alpha0, criticalValues, tVec, iterations)
}

getFisherCombinationCasesCpp <- function(kMax, tVec) {
    .Call(`_rpact_getFisherCombinationCasesCpp`, kMax, tVec)
}

getDesignFisherTryCpp <- function(kMax, alpha, tolerance, criticalValues, scale, alpha0Vec, userAlphaSpending, method) {
    .Call(`_rpact_getDesignFisherTryCpp`, kMax, alpha, tolerance, criticalValues, scale, alpha0Vec, userAlphaSpending, method)
}

getGroupSequentialProbabilitiesCpp <- function(decisionMatrix, informationRates) {
    .Call(`_rpact_getGroupSequentialProbabilitiesCpp`, decisionMatrix, informationRates)
}

getDesignGroupSequentialPampallonaTsiatisCpp <- function(tolerance, beta, alpha, kMax, deltaPT0, deltaPT1, informationRates, sided, bindingFutility) {
    .Call(`_rpact_getDesignGroupSequentialPampallonaTsiatisCpp`, tolerance, beta, alpha, kMax, deltaPT0, deltaPT1, informationRates, sided, bindingFutility)
}

getDesignGroupSequentialUserDefinedAlphaSpendingCpp <- function(kMax, userAlphaSpending, sided, informationRates, bindingFutility, futilityBounds, tolerance) {
    .Call(`_rpact_getDesignGroupSequentialUserDefinedAlphaSpendingCpp`, kMax, userAlphaSpending, sided, informationRates, bindingFutility, futilityBounds, tolerance)
}

getDesignGroupSequentialAlphaSpendingCpp <- function(kMax, alpha, gammaA, typeOfDesign, sided, informationRates, bindingFutility, futilityBounds, tolerance) {
    .Call(`_rpact_getDesignGroupSequentialAlphaSpendingCpp`, kMax, alpha, gammaA, typeOfDesign, sided, informationRates, bindingFutility, futilityBounds, tolerance)
}

getDesignGroupSequentialDeltaWTCpp <- function(kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance, deltaWT) {
    .Call(`_rpact_getDesignGroupSequentialDeltaWTCpp`, kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance, deltaWT)
}

getDesignGroupSequentialPocockCpp <- function(kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance) {
    .Call(`_rpact_getDesignGroupSequentialPocockCpp`, kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance)
}

getDesignGroupSequentialOBrienAndFlemingCpp <- function(kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance) {
    .Call(`_rpact_getDesignGroupSequentialOBrienAndFlemingCpp`, kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance)
}

getDesignGroupSequentialBetaSpendingCpp <- function(criticalValues, kMax, userAlphaSpending, userBetaSpending, informationRates, bindingFutility, tolerance, typeOfDesign, typeBetaSpending, gammaA, gammaB, alpha, beta, sided, betaAdjustment, twoSidedPower) {
    .Call(`_rpact_getDesignGroupSequentialBetaSpendingCpp`, criticalValues, kMax, userAlphaSpending, userBetaSpending, informationRates, bindingFutility, tolerance, typeOfDesign, typeBetaSpending, gammaA, gammaB, alpha, beta, sided, betaAdjustment, twoSidedPower)
}

getDesignGroupSequentialUserDefinedBetaSpendingCpp <- function(criticalValues, kMax, userAlphaSpending, userBetaSpending, sided, informationRates, bindingFutility, tolerance, typeOfDesign, gammaA, alpha, betaAdjustment, twoSidedPower) {
    .Call(`_rpact_getDesignGroupSequentialUserDefinedBetaSpendingCpp`, criticalValues, kMax, userAlphaSpending, userBetaSpending, sided, informationRates, bindingFutility, tolerance, typeOfDesign, gammaA, alpha, betaAdjustment, twoSidedPower)
}

getSimulationMeansLoopCpp <- function(alternative, kMax, maxNumberOfIterations, designNumber, informationRates, futilityBounds, alpha0Vec, criticalValues, meanRatio, thetaH0, stDev, groups, normalApproximation, plannedSubjects, directionUpper, allocationRatioPlanned, minNumberOfSubjectsPerStage, maxNumberOfSubjectsPerStage, conditionalPower, thetaH1, stDevH1, calcSubjectsFunctionType, calcSubjectsFunctionR, calcSubjectsFunctionCpp) {
    .Call(`_rpact_getSimulationMeansLoopCpp`, alternative, kMax, maxNumberOfIterations, designNumber, informationRates, futilityBounds, alpha0Vec, criticalValues, meanRatio, thetaH0, stDev, groups, normalApproximation, plannedSubjects, directionUpper, allocationRatioPlanned, minNumberOfSubjectsPerStage, maxNumberOfSubjectsPerStage, conditionalPower, thetaH1, stDevH1, calcSubjectsFunctionType, calcSubjectsFunctionR, calcSubjectsFunctionCpp)
}

getSimulationRatesCpp <- function(kMax, informationRates, criticalValues, pi1, pi2, maxNumberOfIterations, designNumber, groups, futilityBounds, alpha0Vec, minNumberOfSubjectsPerStage, maxNumberOfSubjectsPerStage, conditionalPower, pi1H1, pi2H1, normalApproximation, plannedSubjects, directionUpper, allocationRatioPlanned, riskRatio, thetaH0, calcSubjectsFunctionType, calcSubjectsFunctionR, calcSubjectsFunctionCpp) {
    .Call(`_rpact_getSimulationRatesCpp`, kMax, informationRates, criticalValues, pi1, pi2, maxNumberOfIterations, designNumber, groups, futilityBounds, alpha0Vec, minNumberOfSubjectsPerStage, maxNumberOfSubjectsPerStage, conditionalPower, pi1H1, pi2H1, normalApproximation, plannedSubjects, directionUpper, allocationRatioPlanned, riskRatio, thetaH0, calcSubjectsFunctionType, calcSubjectsFunctionR, calcSubjectsFunctionCpp)
}

getSimulationSurvivalCpp <- function(designNumber, kMax, sided, criticalValues, informationRates, conditionalPower, plannedEvents, thetaH1, minNumberOfEventsPerStage, maxNumberOfEventsPerStage, directionUpper, allocationRatioPlanned, accrualTime, treatmentGroup, thetaH0, futilityBounds, alpha0Vec, pi1Vec, pi2, eventTime, piecewiseSurvivalTime, cdfValues1, cdfValues2, lambdaVec1, lambdaVec2, phi, maxNumberOfSubjects, maxNumberOfIterations, maxNumberOfRawDatasetsPerStage, kappa, calcEventsFunctionType, calcEventsFunctionR, calcEventsFunctionCpp) {
    .Call(`_rpact_getSimulationSurvivalCpp`, designNumber, kMax, sided, criticalValues, informationRates, conditionalPower, plannedEvents, thetaH1, minNumberOfEventsPerStage, maxNumberOfEventsPerStage, directionUpper, allocationRatioPlanned, accrualTime, treatmentGroup, thetaH0, futilityBounds, alpha0Vec, pi1Vec, pi2, eventTime, piecewiseSurvivalTime, cdfValues1, cdfValues2, lambdaVec1, lambdaVec2, phi, maxNumberOfSubjects, maxNumberOfIterations, maxNumberOfRawDatasetsPerStage, kappa, calcEventsFunctionType, calcEventsFunctionR, calcEventsFunctionCpp)
}

getOneMinusQNorm <- function(p, mean = 0, sd = 1, lowerTail = 1, logP = 0, epsilon = 1.0e-100) {
    .Call(`_rpact_getOneMinusQNorm`, p, mean, sd, lowerTail, logP, epsilon)
}

zeroin <- function(f, lower, upper, tolerance, maxIter) {
    .Call(`_rpact_zeroin`, f, lower, upper, tolerance, maxIter)
}

getCipheredValue <- function(x) {
    .Call(`_rpact_getCipheredValue`, x)
}

getFraction <- function(x, epsilon = 1.0e-6, maxNumberOfSearchSteps = 30L) {
    .Call(`_rpact_getFraction`, x, epsilon, maxNumberOfSearchSteps)
}