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This is a collection of algorithms, a GUI frontend and an R package for analyzinggraphical causal models (DAGs).

The main components of the repository are:

• jslib: a JavaScript library implementing many DAG algorithms. This library underpinsboth the web interface and the R package, but could also be used independently, like in node.js.
• gui: HTML interface for a GUI that exposes most of the functions in the JavaScript library.
• r: R package that exposes most of the functions in the JavaScript library.
• website: The current content ofdagitty.net, including a version of the GUI (which may be older than the one ingui.
• doc: LaTeX source of the dagitty PDF documentation.

Clone the repository and open the filegui/dags.html in your web browser.Currently most functionality should work locally, but you will need an internetconnection if you want to load or save DAG models ondagitty.net.

The R package can be installed from CRAN, but this version is not updated veryfrequently. If you want to install the most recent version of the dagitty R package,you can:

install.packages("remotes") # unless you have it alreadyremotes::install_github("jtextor/dagitty/r")

If you encounter any problems installing the R package,it is probably not due to dagitty itself, but due to thepackage "V8" that it depends on.I may try to remove this dependency in a future version.

You can get more information on dagitty atdagitty.net anddagitty.net/learn. The R package isdocumented through the standard R help interface.There are also a few papers available:

1. Textor, J., van der Zander, B., Gilthorpe, M. S., Liśkiewicz, M., & Ellison, G. T. H. (2017). Robust causal inference using directed acyclic graphs: the R package ‘dagitty.’ In International Journal of Epidemiology (p. dyw341). Oxford University Press (OUP).https://doi.org/10.1093/ije/dyw341

2. Ankan, A., Wortel, I. M. N., & Textor, J. (2021). Testing Graphical Causal Models Using the R Package “dagitty.” In Current Protocols (Vol. 1, Issue 2). Wiley.https://doi.org/10.1002/cpz1.45