DALogisticLogNormal-class: Logistic model with bivariate (log) normal prior and data...
In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

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This is a modified data augmented CRM with logistic regression model using a bivariate normal prior on the intercept and log slope parameters. This class inherits from the normal logistic model class.

We still need to include here formula for the lambda prior.

npiece: the number of pieces in the PEM
l: a vector used in the lambda prior
C_par: a parameter used in the lambda prior, according to Liu's paper, C_par=2 is recommended.
conditionalPEM: is a conditional piecewise-exponential model used? (default) Otherwise an unconditional model is used.

npiece <- 10
Tmax <- 60 # nolintr

lambda_prior <- function(k) {
  npiece / (Tmax * (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
)