ipfr

ipfr-package

<p>The main function is <code><a rd-options="" href="/link/ipu?package=ipfr&version=1.0.2" data-mini-rdoc="ipfr::ipu">ipu</a></code>. For a 2D/matrix problem, the 
<code><a rd-options="" href="/link/ipu_matrix?package=ipfr&version=1.0.2" data-mini-rdoc="ipfr::ipu_matrix">ipu_matrix</a></code> function is easier to use. The resulting
<code>weight_tbl</code> from <code>ipu()</code> can be fed into <code><a rd-options="" href="/link/synthesize?package=ipfr&version=1.0.2" data-mini-rdoc="ipfr::synthesize">synthesize</a></code>
to generate a synthetic population</p>

Performs iterative proportional updating given a seed table and
an arbitrary number of marginal distributions. This is commonly used in
population synthesis, survey raking, matrix rebalancing, and other
applications. For example, a household survey may be weighted to match the
known distribution of households by size from the census. An origin/
destination trip matrix might be balanced to match traffic counts.
The approach used by this package is based on a paper from
Arizona State University (Ye, Xin, et. al. (2009)
<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.537.723&rep=rep1&type=pdf>).
Some enhancements have been made to their work including primary and
secondary target balance/importance, general marginal agreement, and weight
restriction.

Kyle Ward

List Balancing for Reweighting and Population Synthesis

Greg Macfarlane

ipfr function

<p>The main function is <code><a rd-options='' href='ipu'>ipu</a></code>. For a 2D/matrix problem, the 
<code><a rd-options='' href='ipu_matrix'>ipu_matrix</a></code> function is easier to use. The resulting
<code>weight_tbl</code> from <code>ipu()</code> can be fed into <code><a rd-options='' href='synthesize'>synthesize</a></code>
to generate a synthetic population</p>

ipfr: ipfr: A package to perform iterative proportional fitting

Description

Arguments

See Also