The evaluation of the design candidate is independent of the optimization algorithm used.

```
evaluate_design_candidate(
utility,
design_candidate,
prior_values,
design_env,
model,
dudx,
return_all,
significance
)
```

- utility
A utility function

- design_candidate
The current design candidate

- prior_values
a list or vector of assumed priors

- design_env
A design environment in which to evaluate the the function to derive the variance-covariance matrix.

- model
A character string indicating the model to optimize the design for. Currently the only model programmed is the 'mnl' model and this is also set as the default.

- dudx
A character string giving the name of the parameter in the denominator. Must be specified when optimizing for 'c-error'

- return_all
If `TRUE` return a K or K-1 vector with parameter specific error measures. Default is `FALSE`.

- significance
A t-value corresponding to the desired level of significance. The default is significance at the 5 t-value of 1.96.

A named vector with efficiency criteria of the current design candidate. If Bayesian prior_values are used, then it returns the average error.