transfer from old repo

apps/shinyDAG-1.0.0/www/examples/README.md

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+## Creating Examples
+
+To create a new example, run ShinyDAG locally using the shinydag dev docker file.
+
+Set up your example and then save it as a bookmark.
+
+Note the URL created by shiny for the bookmark, it should end with
+
+```
+?_state_id_=9d49cb0ba72b00f2
+```
+
+Navigate to the `shiny_bookmarks` folder and find the folder with the bookmark token, e.g. `9d49cb0ba72b00f2`.
+
+Copy `values.rds` to `www/examples` and give the file a descriptive name.
+These names are used for the shiny inputs, so keep the characters sane (and no spaces).
+
+Also, save the DAG image into `www/examples` with the same name (not required but a good idea).
+
+Finally, add the description text to `www/examples/examples.yml`.
+Here's an example template that you can copy.
+Note that if you can use HTML in the `description`, but it needs to be valid or it will cause problems on the page.
+
+```yaml
+- name: Classic Confounding
+  description: >
+    This is a description of classic confounding. Descriptions may include
+    <strong>HTML</strong>.
+  file: classic-confounding.rds
+  image: classic-confounding.png
+```

apps/shinyDAG-1.0.0/www/examples/examples.yml

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+- name: Classic Confounding
+  description: >
+    This depicts <em>classic confounding</em>, where a confounder C is a common cause of exposure (E) and outcome (Y).
+  file: classic-confounding.rds
+  image: classic-confounding.png
+  
+- name: Differential Loss to Follow-Up
+  description: >
+    This depicts <em>differential loss to follow up</em>, where patients may be censored (C) depending on their value of L.
+  file: differential-loss-to-follow-up.rds
+  image: differential-loss-to-follow-up.png
+
+- name: Mediator with Confounding
+  description: >
+    This shows a <em>mediator with confounding</em>, where the variable M mediates the effect of E on Y, which is confounded by the variable C.
+  file: mediator-with-confounding.rds
+  image: mediator-with-confounding.png
+  
+- name: Selection Bias
+  description: >
+    This depicts classic <em>selection bias</em>, where C denotes criteria under which the data are observed which, in turn, is a downstream consequence of exposure (E) and outcome (D).
+  file: selection-bias.rds
+  image: selection-bias.png