either maximize or minimize the log-likelihood function. If estimation is successful, the function calculates a high-precision numerical hessian and a standard and robust variance-covariance matrix.

estimate(ll, estim_env, model_options, control = NULL)



This is the 'raw' log-likelihood function passed to the estimation routine. It is important that the user takes into account whether the optimization routine is a minimizer (e.g. 'ucminf') or a maximizer (e.g. 'maxlik').


An estimation environment or list of estimation environments returned from prepare


A list of model options. Note, this list is validated for a second time here to set some necessary defaults. See validate for details.


A list of control options that are passed to set_controls.


A 'cmdlr' model object


NOTE: There are no checks on whether the log-likelihood function is correctly specified. If you see that your log-likelihood value is getting progressively worse from starting values, check your 'll' specification and that it corresponds to the chosen optimizer.


if (FALSE) {
  # See /examples for how to use
  estimate(ll, estim_env, model_options)
  # or 
  estimate(ll, estim_env, model_options, control)