usage: [theta, V, obj_value, infocrit] =
    mle_results(theta, data, model, modelargs, names, title, unscale, control)

 inputs:
 theta: column vector of model parameters
 data: data matrix
 model: name of function that computes log-likelihood
 modelargs: (cell) additional inputs needed by model. May be empty ("")
 names: vector of parameter names, e.g., use names = char("param1", "param2");
 title: string, describes model estimated
 unscale: (optional) cell that holds means and std. dev. of data (see scale_data)
 control: (optional) BFGS or SA controls (see bfgsmin and samin). May be empty ("").
 nslaves: (optional) number of slaves if executed in parallel (requires MPITB)

 outputs:
 theta: ML estimated value of parameters
 obj_value: the value of the log likelihood function at ML estimate
 conv: return code from bfgsmin (1 means success, see bfgsmin for details)
 iters: number of BFGS iteration used

 Please see mle_example for information on how to use this

Package: econometrics