fix encoding error on winbuilder

r-causal · Mar 26, 2018 · 7efd02b · 7efd02b
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diff --git a/docs/articles/intro-to-dags.html b/docs/articles/intro-to-dags.html
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diff --git a/vignettes/bias-structures.Rmd b/vignettes/bias-structures.Rmd
@@ -249,5 +249,5 @@ But follow-up is downstream from a collider, symptoms. Controlling for a downstr
 
 Many, if not all, forms of systematic bias can be drawn as DAGs. Here are a few sources with interesting examples:
 
-* Miguel Hernán's course on DAGs includes a number of examples on common structures of bias: [Causal Diagrams: Draw Your Assumptions Before Your Conclusions](https://www.edx.org/course/causal-diagrams-draw-assumptions-harvardx-ph559x). Also see his article [A structural approach to selection bias](https://www.ncbi.nlm.nih.gov/pubmed/15308962).
+* Miguel Hern&aacute;n's course on DAGs includes a number of examples on common structures of bias: [Causal Diagrams: Draw Your Assumptions Before Your Conclusions](https://www.edx.org/course/causal-diagrams-draw-assumptions-harvardx-ph559x). Also see his article [A structural approach to selection bias](https://www.ncbi.nlm.nih.gov/pubmed/15308962).
 * The chapter on DAGs in [Modern Epidemiology](https://books.google.com/books/about/Modern_Epidemiology.html?id=Z3vjT9ALxHUC) includes a couple of the examples here and many more directly related to conducting observational research, including measurement error, selection bias, residual confounding, and missing data.
diff --git a/vignettes/intro-to-dags.Rmd b/vignettes/intro-to-dags.Rmd
@@ -176,7 +176,7 @@ Now smoking and cardiac arrest are d-separated. Since our question is about the
 
 # Resources
 
-* Miguel Hernán, who has written extensively on the subject of causal inference and DAGs, has an accessible course on edx that teaches the use of DAGs for causal inference: [Causal Diagrams: Draw Your Assumptions Before Your Conclusions](https://www.edx.org/course/causal-diagrams-draw-assumptions-harvardx-ph559x). Also see his article [The Hazard of Hazard Ratios](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3653612/) for more on issues with hazard ratios in causal inference.
+* Miguel Hern&aacute;n, who has written extensively on the subject of causal inference and DAGs, has an accessible course on edx that teaches the use of DAGs for causal inference: [Causal Diagrams: Draw Your Assumptions Before Your Conclusions](https://www.edx.org/course/causal-diagrams-draw-assumptions-harvardx-ph559x). Also see his article [The Hazard of Hazard Ratios](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3653612/) for more on issues with hazard ratios in causal inference.
 * Julia Rohrer has a very readable paper introducing DAGs, mostly from the perspective of psychology: [Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data](http://journals.sagepub.com/doi/abs/10.1177/2515245917745629)
 * Judea Pearl also has a number of texts on the subject of varying technical difficulty. A good place to start is [Causal Inference in Statistics: A Primer](http://bayes.cs.ucla.edu/PRIMER/). See also his article with Sander Greenland and James Robins on collapsibility: [Confounding and Collapsibility in Causal Inference](https://www.jstor.org/stable/2676645?seq=1#page_scan_tab_contents).
 * If you're an epidemiologist, I also recommend the chapter on DAGs in [Modern Epidemiology](https://books.google.com/books/about/Modern_Epidemiology.html?id=Z3vjT9ALxHUC).