Quantiles2LogisticNormal: Convert prior quantiles (lower, median, upper) to logistic...
In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

This function uses generalized simulated annealing to optimize a LogisticNormal model to be as close as possible to the given prior quantiles.

Quantiles2LogisticNormal(
  dosegrid,
  refDose,
  lower,
  median,
  upper,
  level = 0.95,
  logNormal = FALSE,
  parstart = NULL,
  parlower = c(-10, -10, 0, 0, -0.95),
  parupper = c(10, 10, 10, 10, 0.95),
  seed = 12345,
  verbose = TRUE,
  control = list(threshold.stop = 0.01, maxit = 50000, temperature = 50000, max.time =
    600)
)

`dosegrid`	the dose grid
`refDose`	the reference dose
`lower`	the lower quantiles
`median`	the medians
`upper`	the upper quantiles
`level`	the credible level of the (lower, upper) intervals (default: 0.95)
`logNormal`	use the log-normal prior? (not default) otherwise, the normal prior for the logistic regression coefficients is used
`parstart`	starting values for the parameters. By default, these are determined from the medians supplied.
`parlower`	lower bounds on the parameters (intercept alpha and the slope beta, the corresponding standard deviations and the correlation.)
`parupper`	upper bounds on the parameters
`seed`	seed for random number generation
`verbose`	be verbose? (default)
`control`	additional options for the optimisation routine, see `GenSA` for more details

a list with the best approximating model (LogisticNormal or LogisticLogNormal), the resulting quantiles, the required quantiles and the distance to the required quantiles, as well as the final parameters (which could be used for running the algorithm a second time)