get_control

This function provides a way to merge the user specified controls for the
optimization methods with their respective default controls.

internal

An implementation of the Monte Carlo techniques described in details by Dufour (2006) <doi:10.1016/j.jeconom.2005.06.007> and Dufour and Khalaf (2007) <doi:10.1002/9780470996249.ch24>. The two main features available are the Monte Carlo method with tie-breaker, mc(), for discrete statistics, and the Maximized Monte Carlo, mmc(), for statistics with nuisance parameters.

Gabriel Rodriguez Rondon

MaxMC

Maximized Monte Carlo

Julien Neves

Jean-Marie Dufour

Gabriel Rodriguez-Rondon

get_control function

<dl><dt>method</dt>
<dd>A character string. Type of algorithm to be
used for global optimization. The four available methods
are simulated annealing (<code>GenSA</code>), particle swarm (<code>psoptim</code>),
genetic algorithm (<code>GA</code>), and grid search (<code>gridSearch</code>)
Default is <code>GenSA</code>,</dd>
<dt>control</dt>
<dd>A list. Arguments to be used to control the
behavior of the algorithm chosen in <code>method</code>. See controls section for more details.</dd></dl>

Arguments

Format the optimization method controls — get_control

<dl>

<dt>method</dt>
<dd>A character string. Type of algorithm to be
used for global optimization. The four available methods
are simulated annealing (<code>GenSA</code>), particle swarm (<code>psoptim</code>),
genetic algorithm (<code>GA</code>), and grid search (<code>gridSearch</code>)
Default is <code>GenSA</code>,</dd>


<dt>control</dt>
<dd>A list. Arguments to be used to control the
behavior of the algorithm chosen in <code>method</code>. See controls section for more details.</dd>

</dl>

get_control: Format the optimization method controls

Description

Usage

Value

Arguments