f.lik.con: Calculate likelihood for continuous response
In alfcrisci/bmdModeling: Benchmark dose modeling

Calculate likelihood for continuous response

f.lik.con(theta, x, y, dtype, fct1, fct2, fct3, model.ans, mn.log, sd2.log, nn,
  Vdetlim, CES, twice = TRUE, ttt = 0, fct4 = 1, fct5 = 1,
  cens.up = NA, lb = -Inf, ub = Inf, par.tmp, increase = increase,
  x.mn = NA, track = FALSE)

`theta`	numeric vector, the initial regression parameter values
`x`	numeric vector, the dose values
`y`	numeric vector, the response values
`dtype`	integer, determines the type of response
`fct1`	numeric, value for parameter a
`fct2`	numeric, value for parameter b
`fct3`	numeric, value for parameter var
`model.ans`	integer, determines the model that will be fitted
`mn.log`	numeric vector, transformation of the response values, see f.execute()
`sd2.log`	numeric vector, transformation of the sd of the response values, see f.execute()
`nn`	numeric vector, the number of responses per dose level, for continuous summary data
`Vdetlim`	numeric vector, values of detection limit
`CES`	numeric, value for the CES
`twice`	boolean, if some parameter values are equal, see f.execute()
`ttt`	numeric, time variable
`fct4`	numeric, value for parameter c
`fct5`	numeric, value for parameter d
`cens.up`	numeric, value for right censoring
`lb`	numeric vector, determines the lower bound for theta; default value is -Inf
`ub`	numeric vector, determines the upper bound for theta; default value is Inf
`par.tmp`	numeric vector, regression parameter values, see f.pars()
`increase`	boolean, whether the response values are increasing or decreasing for increasing dose values
`x.mn`	numeric value, the mean of dose values, see f.execute()
`track`	logical, if TRUE (FALSE by default) print the name of the function which is currently being run