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+## Copyright (C) 2003,2004  Michael Creel <michael.creel@uab.es>
+##
+## This program is free software; you can redistribute it and/or modify
+## it under the terms of the GNU General Public License as published by
+## the Free Software Foundation; either version 2 of the License, or
+## (at your option) any later version.
+##
+## This program is distributed in the hope that it will be useful,
+## but WITHOUT ANY WARRANTY; without even the implied warranty of
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+## GNU General Public License for more details.
+##
+## You should have received a copy of the GNU General Public License
+## along with this program; If not, see <http://www.gnu.org/licenses/>.
+
+## Example to show how to use MLE functions
+
+
+
+# Generate data
+n = 1000; # how many observations?
+
+# the explanatory variables: note that they have unequal scales
+x = [ones(n,1) -rand(n,1) randn(n,1)];
+theta = 1:3; # true coefficients are 1,2,3
+theta = theta';
+
+lambda = exp(x*theta);
+y = poissrnd(lambda); # generate the dependent variable
+
+####################################
+# define arguments for mle_results #
+####################################
+
+# starting values
+theta = zeros(3,1);
+# data
+data = [y, x];
+# name of model to estimate
+model = "poisson";
+modelargs = {0}; # if this is zero the function gives analytic score, otherwise not
+# parameter names
+names = char("beta1", "beta2", "beta3");
+mletitle = "Poisson MLE trial"; # title for the run
+
+# controls for bfgsmin: 30 iterations is not always enough for convergence
+control = {50,0};
+
+# This displays the results
+printf("\n\nanalytic score, unscaled data\n");
+[theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, mletitle, 0, control);
+
+# This just calculates the results, no screen display
+printf("\n\nanalytic score, unscaled data, no screen display\n");
+theta = zeros(3,1);
+[theta, obj_value, convergence] = mle_estimate(theta, data, model, modelargs, control);
+printf("obj_value = %f, to confirm it worked\n", obj_value);
+
+# This show how to scale data during estimation, but get results
+# for data in original units (recommended to avoid conditioning problems)
+# This usually converges faster, depending upon the data
+printf("\n\nanalytic score, scaled data\n");
+[scaled_x, unscale] = scale_data(x);
+data = [y, scaled_x];
+theta = zeros(3,1);
+[theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, mletitle, unscale, control);
+
+# Example using numeric score
+printf("\n\nnumeric score, scaled data\n");
+theta = zeros(3,1);
+modelargs = {1}; # set the switch for no score
+[theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, mletitle, unscale, control);
+
+# Example doing estimation in parallel on a cluster (requires MPITB)
+# uncomment the following if you have MPITB installed
+# theta = zeros(3,1);
+# nslaves = 1;
+# title = "MLE estimation done in parallel";
+# [theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, mletitle, unscale, control, nslaves);