h_get_design_dualresponses <- function(placebo = FALSE) {
  data <- DataDual(doseGrid = c(10, seq(25, 300, 25)), placebo = placebo)
  DLEmodel <- LogisticIndepBeta(
    binDLE = c(1.05, 1.8),
    DLEweights = c(3, 3),
    DLEdose = c(25, 300),
    data = data
  )
  Effmodel <- Effloglog(
    eff = c(1.223, 2.513),
    eff_dose = c(25, 300),
    nu = c(a = 1, b = 0.025),
    data = data
  )
  design <- DualResponsesDesign(
    nextBest = NextBestMaxGain(
      prob_target_drt = 0.35,
      prob_target_eot = 0.3
    ),
    model = DLEmodel,
    eff_model = Effmodel,
    stopping = StoppingMinPatients(nPatients = 36),
    increments = IncrementsRelative(
      intervals = c(25, 300),
      increments = c(2, 2)
    ),
    cohort_size = CohortSizeConst(size = 3),
    data = data,
    startingDose = 25
  )
  if (placebo) {
    design@pl_cohort_size <- CohortSizeConst(size = 1)
  }
  design
}

h_get_design_tddesign <- function(placebo = FALSE) {
  dosegrid <- seq(25, 300, 25)
  if (placebo) {
    dosegrid <- c(10, dosegrid)
  }
  data <- Data(doseGrid = dosegrid, placebo = placebo)

  model <- LogisticIndepBeta(
    binDLE = c(1.05, 1.8),
    DLEweights = c(3, 3),
    DLEdose = c(25, 300),
    data = data
  )
  tdNextBest <- NextBestTD(
    prob_target_drt = 0.35,
    prob_target_eot = 0.3
  )
  doseRecommendation <- nextBest(
    tdNextBest,
    doselimit = max(data@doseGrid),
    model = model,
    data = data
  )
  emptydata <- Data(doseGrid = seq(25, 300, 25))

  design <- TDDesign(
    model = model,
    nextBest = tdNextBest,
    stopping = StoppingMinPatients(nPatients = 36),
    increments = IncrementsRelative(
      intervals = c(min(data@doseGrid), max(data@doseGrid)),
      increments = c(2, 2)
    ),
    cohort_size = CohortSizeConst(size = 3),
    data = data,
    startingDose = 50
  )
  if (placebo) {
    design@pl_cohort_size <- CohortSizeConst(size = 1)
  }
  design
}

h_get_design_dualdata <- function(placebo = FALSE) {
  # Define the dose-grid
  emptydata <- DataDual(
    doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100),
    placebo = placebo
  )

  # Initialize the CRM model
  model <- DualEndpointRW(
    mean = c(0, 1),
    cov = matrix(c(1, 0, 0, 1), nrow = 2),
    sigma2betaW = 0.01,
    sigma2W = c(a = 0.1, b = 0.1),
    use_log_dose = TRUE,
    ref_dose = 2,
    rho = c(a = 1, b = 1),
    rw1 = TRUE
  )

  # Choose the rule for selecting the next dose
  myNextBest <- NextBestDualEndpoint(
    target = c(0.9, 1),
    overdose = c(0.35, 1),
    max_overdose_prob = 0.25
  )

  # Choose the rule for stopping
  myStopping4 <- StoppingTargetBiomarker(
    target = c(0.9, 1),
    prob = 0.5
  )
  myStopping <- myStopping4 | StoppingMinPatients(10) | StoppingMissingDose()

  # Choose the rule for dose increments
  myIncrements <- IncrementsRelative(
    intervals = c(0, 20),
    increments = c(1, 0.33)
  )

  # Initialize the design
  design <- DualDesign(
    model = model,
    data = emptydata,
    nextBest = myNextBest,
    stopping = myStopping,
    increments = myIncrements,
    cohort_size = CohortSizeConst(3),
    startingDose = 3
  )
  if (placebo) {
    design@pl_cohort_size <- CohortSizeConst(1)
  }
  design
}

h_get_design_data <- function(placebo = FALSE) {
  # Define the dose-grid
  emptydata <- Data(
    doseGrid = c(0.1, 1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100),
    placebo = placebo
  )

  # Initialize the CRM model
  model <- LogisticLogNormal(
    mean = c(-0.85, 1),
    cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
    ref_dose = 56
  )

  # Choose the rule for selecting the next dose
  myNextBest <- NextBestNCRM(
    target = c(0.2, 0.35),
    overdose = c(0.35, 1),
    max_overdose_prob = 0.25
  )

  # Choose the rule for stopping
  myStopping1 <- StoppingMinCohorts(nCohorts = 3)
  myStopping2 <- StoppingTargetProb(
    target = c(0.2, 0.35),
    prob = 0.5
  )
  myStopping3 <- StoppingMinPatients(nPatients = 20)
  myStopping <- (myStopping1 & myStopping2) |
    myStopping3 |
    StoppingMissingDose()

  # Choose the rule for dose increments
  myIncrements <- IncrementsRelative(
    intervals = c(0, 20, 50),
    increments = c(1, 0.67, 0.33)
  )

  # Initialize the design
  design <- Design(
    model = model,
    nextBest = myNextBest,
    stopping = myStopping,
    increments = myIncrements,
    cohort_size = CohortSizeConst(3),
    data = emptydata,
    startingDose = 3
  )
  if (placebo) {
    design@pl_cohort_size <- CohortSizeConst(1)
  }
  design
}

h_get_design_da <- function(placebo = FALSE) {
  emptydata <- DataDA(
    doseGrid = c(0.1, 0.5, 1, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
    Tmax = 60,
    placebo = placebo
  )

  npiece_ <- 10
  t_max <- 60

  lambda_prior <- function(k) {
    npiece_ / (t_max * (npiece_ - k + 0.5))
  }

  model <- DALogisticLogNormal(
    mean = c(-0.85, 1),
    cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
    ref_dose = 56,
    npiece = npiece_,
    l = as.numeric(t(apply(
      as.matrix(c(1:npiece_), 1, npiece_),
      2,
      lambda_prior
    ))),
    c_par = 2
  )

  # Choose the rule for dose increments
  myIncrements <- IncrementsRelative(
    intervals = c(0, 20),
    increments = c(1, 0.33)
  )

  myNextBest <- NextBestNCRM(
    target = c(0.2, 0.35),
    overdose = c(0.35, 1),
    max_overdose_prob = 0.25
  )

  # Choose the rule for the cohort-size
  mySize1 <- CohortSizeRange(
    intervals = c(0, 30),
    cohort_size = c(1, 3)
  )
  mySize2 <- CohortSizeDLT(
    intervals = c(0, 1),
    cohort_size = c(1, 3)
  )
  mySize <- maxSize(mySize1, mySize2)

  # Choose the rule for stopping
  myStopping1 <- StoppingTargetProb(
    target = c(0.2, 0.35),
    prob = 0.5
  )
  myStopping2 <- StoppingMinPatients(nPatients = 50)

  myStopping <- (myStopping1 | myStopping2)

  # Choose the safety window
  mysafetywindow <- SafetyWindowConst(c(6, 2), 7, 7)

  # Initialize the design
  design <- DADesign(
    model = model,
    increments = myIncrements,
    nextBest = myNextBest,
    stopping = myStopping,
    cohort_size = mySize,
    data = emptydata,
    safetyWindow = mysafetywindow,
    startingDose = 3
  )
  if (placebo) {
    design@pl_cohort_size <- CohortSizeConst(1)
  }

  design
}