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		<div class="page-header"><h1>tmlenet R Package</h1></div>
		<div class="well small">Permalink: <a class="more" href="/tmlenet-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/tmle.html">TMLE</a> <a href="/tag/networks.html">networks</a> </div>
		<div><p>The <a href="https://github.com/osofr/tmlenet">tmlenet R package</a> implements Targeted Maximum Likelihood Estimation (TMLE) for network data.  The package performs estimation of average causal effects for single time point interventions in network-dependent (non-IID) data in the presence of interference and/or spillover.  Currently implemented estimation algorithms are the targeted maximum likelihood estimation (TMLE), Horvitz-Thompson or the inverse-probability-of-treatment (IPTW) estimator and the parametric G-computation estimator. The user-specified interventions can be either static, dynamic or stochastic. Asymptotically correct influence-curve-based confidence intervals are also constructed for the TMLE and IPTW. See the paper below for more information on the estimation methodology employed by the R package:</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</p>
<p><a href="https://github.com/osofr/tmlenet">GitHub</a> | <a href="https://cran.r-project.org/web/packages/tmlenet/index.html">CRAN</a> </p></div>
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