The simple search algorithm is essentially an evaluation of a large number of randomly generated vectors of starting values. Each vector is evaluated at the starting values and sorted in descending order. The best fitting vectors of starting values are returned.
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').
A log-likelihood expression prepared by
A list of freely estimated parameters
An integer giving the number of candidates to evaluate. Default is 100.
An integer giving the number of parameter vectors to return. The default is 10
A double indicating a multiplier for the 'simple' search algorithm. The default is 1.
A list of workers
A matrix of starting values (free parameters only)