From 5157f2d0aa90ef3033e7ec2f0487bfb03a37e4aa Mon Sep 17 00:00:00 2001
From: desktop <jallen108@gsu.edu>
Date: Wed, 28 Jun 2023 09:25:08 -0400
Subject: [PATCH] Fixing links for dags

---
 .DS_Store        | Bin 0 -> 10244 bytes
 example/dags.qmd |   8 ++++----
 2 files changed, 4 insertions(+), 4 deletions(-)
 create mode 100644 .DS_Store

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..665bf9330834e75b7dfa0c7b8096ef0c82e154d3
GIT binary patch
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diff --git a/example/dags.qmd b/example/dags.qmd
index 694f65a5..8da7351d 100644
--- a/example/dags.qmd
+++ b/example/dags.qmd
@@ -15,7 +15,7 @@ This video shows how to use it:
 
 ## DAGs with R, ggdag, and dagitty
 
-You can use the [**ggdag**](https://ggdag.malco.io/) and [**dagitty**](http://dagitty.net/primer/) packages in R to build and work with DAGs too. I typically draw an initial DAG in my browser with dagitty.net and then I rewrite it in code in R so that it's more official and formal and reproducible.
+You can use the [**ggdag**](https://r-causal.github.io/ggdag/index.html) and [**dagitty**](http://dagitty.net/primer/) packages in R to build and work with DAGs too. I typically draw an initial DAG in my browser with dagitty.net and then I rewrite it in code in R so that it's more official and formal and reproducible.
 
 ### Live coding example
 
@@ -43,9 +43,9 @@ library(dagitty)    # For working with DAG logic
 
 The general process for making and working with DAGs in R is to create a DAG object with `dagify()` and then plot it with `ggdag()`. The documentation for **ggdag** is really good and helpful and full of examples. Check these pages for all sorts of additional details:
 
-- ["An introduction to ggdag"](https://ggdag.malco.io/articles/intro-to-ggdag.html)
-- ["An introduction to directed acyclic graphs"](https://ggdag.malco.io/articles/intro-to-dags.html)
-- [List of all ggdag-related functions](https://ggdag.malco.io/reference/index.html)
+- ["An introduction to ggdag"](https://r-causal.github.io/ggdag/articles/intro-to-ggdag.html)
+- ["An introduction to directed acyclic graphs"](https://r-causal.github.io/ggdag/articles/intro-to-dags.html)
+- [List of all ggdag-related functions](https://r-causal.github.io/ggdag/reference/index.html)
 
 The syntax for `dagify()` is similar to the formula syntax you've been using for building regression models with `lm()`. The formulas you use in `dagify()` indicate the relationships between nodes.