bgw_itsum

Prints iteration summary, info on initial and final x.

Performs statistical estimation and inference-related computations
by accessing and executing modified versions of 'Fortran' subroutines
originally published in the Association for Computing Machinery (ACM)
journal Transactions on Mathematical Software (TOMS) by Bunch, Gay and
Welsch (1993) <doi:10.1145/151271.151279>. The acronym 'BGW' (from the
authors' last names) will be used when making reference to technical
content (e.g., algorithm, methodology) that originally appeared in ACM TOMS.
A key feature of BGW is that it exploits the special structure of statistical
estimation problems within a trust-region-based optimization approach to
produce an estimation algorithm that is much more effective than the usual
practice of using optimization methods and codes originally developed for
general optimization. The 'bgw' package bundles 'R' wrapper (and related)
functions with modified 'Fortran' source code so that it can be compiled and
linked in the 'R' environment for fast execution. This version implements
a function ('bgw_mle.R') that performs maximum likelihood estimation (MLE)
for a user-provided model object that computes probabilities (a.k.a.
probability densities). The original motivation for producing this package
was to provide fast, efficient, and reliable MLE for discrete choice models
that can be called from the 'Apollo' choice modelling 'R' package (
see <https://www.apollochoicemodelling.com>). Starting with the release of
Apollo 3.0, BGW is the default estimation package. However, estimation can also
be performed using BGW in a stand-alone fashion without using 'Apollo'
(as shown in simple examples included in the package). Note also that BGW
capabilities are not limited to MLE, and future extension to other estimators
(e.g., nonlinear least squares, generalized method of moments, etc.) is possible.
The 'Fortran' code included in 'bgw' was modified by one of the
original BGW authors (Bunch) under his rights as confirmed by direct
consultation with the ACM Intellectual Property and Rights Manager. See
<https://authors.acm.org/author-resources/author-rights>. The main
requirement is clear citation of the original publication (see above).

David S. Bunch

Bunch-Gay-Welsch Statistical Estimation

David M. Gay

Roy E. Welsch

Stephane Hess

David Palma

bgw_itsum function

<dl><dt>d</dt>
<dd>Scaling vector</dd>
<dt>g</dt>
<dd>Gradient of objective function (negative-log-likelihood) wrt x</dd>
<dt>iv</dt>
<dd>BGW internal vector of integer values</dd>
<dt>v</dt>
<dd>BGW internal vector of numeric values</dd>
<dt>x</dt>
<dd>Parameter vector for which the objective function is being minimized</dd>
<dt>p</dt>
<dd>Dimension of x</dd>
<dt>betaIsNamed</dt>
<dd>Logical.</dd>
<dt>betaNames</dt>
<dd>Character vector. If available, has beta parameter names.</dd>
<dt>i_itsum</dt>
<dd>Code from caller to select specific options</dd></dl>

Arguments

bgw_itsum — bgw_itsum

<dl>

<dt>d</dt>
<dd>Scaling vector</dd>


<dt>g</dt>
<dd>Gradient of objective function (negative-log-likelihood) wrt x</dd>


<dt>iv</dt>
<dd>BGW internal vector of integer values</dd>


<dt>v</dt>
<dd>BGW internal vector of numeric values</dd>


<dt>x</dt>
<dd>Parameter vector for which the objective function is being minimized</dd>


<dt>p</dt>
<dd>Dimension of x</dd>


<dt>betaIsNamed</dt>
<dd>Logical.</dd>


<dt>betaNames</dt>
<dd>Character vector. If available, has beta parameter names.</dd>


<dt>i_itsum</dt>
<dd>Code from caller to select specific options</dd>

</dl>

bgw_itsum: bgw_itsum

Description

Usage

Value

Arguments