update refs in vignette

osofr · Feb 21, 2016 · b3b2b29 · b3b2b29
1 parent 63cbc90
commit b3b2b29
Showing 1 changed file with 6 additions and 4 deletions.
diff --git a/vignettes/tmlenet_vignette.Rmd b/vignettes/tmlenet_vignette.Rmd
@@ -10,9 +10,6 @@ output:
     number_sections: true
     highlight: haddock
     keep_tex: true
-    pandoc_args: [
-      "--natbib"
-    ]
     latex_engine: pdflatex
 fontsize: 10pt
 bibliography: 
@@ -23,6 +20,11 @@ vignette: >
   \usepackage[utf8]{inputenc}
   \usepackage{float}
 ---
+<!--
+    pandoc_args: [
+      "--natbib"
+    ]
+     -->
 <!--   - SimCausal_2014.bib
   - R-Pckgs.bib
   - TMLE_networks_2014.bib -->
@@ -87,7 +89,7 @@ vignette: >
 # Introduction
 <!-- ************************************************ -->
 
-This vignette describes how to use \texttt{tmlenet} \textbf{R} package [\cite{R-tmlenet}] to estimate the effects of single time point stochastic interventions using such data. The `tmlenet` R 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. For more information behind the methods implemented the `tmlenet` package, see @sofryginTechreport; @vdL2014nets.
+This vignette describes how to use \texttt{tmlenet} \textbf{R} package [@R-tmlenet] to estimate the effects of single time point stochastic interventions using such data. The `tmlenet` R 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. For more information behind the methods implemented the `tmlenet` package, see @sofryginTechreport and @vdL2014nets.
 
 
 The examples in this vignette will also involve network data simulations that were performed with `simcausal` R package [@R-simcausal]. To learn more about the general functionality of the `simcausal` package, see the corresponding vignette titled: "simcausal Package: Simulations with Complex Longitudinal Data". To learn more about using the `simcausal` package specifically for conducting the network-based simulations, see the vignette "simcausal Package: Simulations with Networks-Based Dependent-Data Structural Equation Models".