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		<div class="page-header"><h1>simcausal R Package</h1></div>
		<div class="well small">Permalink: <a class="more" href="/simcausal-r-package.html">2015-11-22 22:41:00-08:00</a>
by <a class="url fn" href="/author/admin.html">admin </a>
 in <a href="/category/software.html">Software</a>
tags: <a href="/tag/r.html">R</a> <a href="/tag/causal-inference.html">causal inference</a> <a href="/tag/longitudinal.html">longitudinal</a> </div>
		<div><p>The <a href="https://github.com/osofr/simcausal">simcausal R package</a> is a tool for specification and simulation of complex longitudinal data structures that are based on structural equation models (SEMs). The emphasis is on the types of simulations frequently encountered in causal inference problems, such as, observational data with time-dependent confounding, selection bias, and random monitoring processes. The interface allows for quick expression of dependencies between a large number of time-varying nodes. </p>
<p>M. J. van der Laan, “Causal inference for a population of causally connected units,” J. Causal Inference J. Causal Infer., vol. 2, no. 1, pp. 13–74, 2014.</p>
<p><strong>Author(s)</strong>: Oleg Sofrygin, Mark van der Laan, Romain Neugebauer</p>
<p><a href="https://github.com/osofr/simcausal">GitHub</a> | <a href="https://cran.r-project.org/web/packages/simcausal/index.html">CRAN</a> </p></div>
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