• Tetrad itself has undergone major revisions since January 2022. Many issues have been fixed in the underlying Tetrad library and the Application. This is not to say that all issues have been fixed--please, if you see something, say something! We continue to work (with a very small team!) on improvement. But there has been a lot of progress. For a list of updates since that time, please see our Releases. This work is very much ongoing. New features added to the Application recently include a Plot Matrix tool and a Markov Checker, in addition (as mentioned) to several new algorithms.
• An offshoot of Tetrad, causal-learn, in Python only, was initiated by Dr. Kun Zhang and is currently managed by Yujia Zheng, with contributors both in our department and from around the world. This started out with the goal of translating some basic algorithms from Tetrad into Python but has branched off into new directions, making it a distinct project. It is now part of the Py-Why Python space.
• For those who wish to access the underlying Java Tetrad library from Python, we have put together a Python package to do this, Py-Tetrad. A workshop paper for is available. This is new as of April 2023. It gives access to arbitrary Tetrad library code in Python.
• For those who wish to access the underlying Java Tetrad library from R, we have put together an R package RPy-tetrad. The same workshop paper above describes both Py-Tetrad and RPy-Tetrad. This is also new as of April 2023. This does not support arbitrary Tetrad library code but does capture a wide swath of it.
• We have, for some time now, had a command-line version of Tetrad available, Causal Command. This has undergone a number of recent updates and is kept current with the latest version of Tetrad. All Tetrad algorithms are accessible from the command line using this tool, and there is a robust help facility.
• We maintain a Repository of Example Causal Datasets using a consistent file format. Feel free to peruse this and suggest new public datasets to include, or if you know it, new ground truth facts. 
• We have spent a little time thinking of how to update the Tetrad application to a more contemporary look and feel, consistent with other applications from recent years. This project, Tetrad-FX, gives access to the same Tetrad library as Tetrad. It changes the focus of Tetrad somewhat away from simulation-centric and more to real-data-centric. We intend to include tools and library updates for this that will be useful for analyzing real data. A first draft is finished for consideration, but more work must be done to make it useful for practical work. As part of Tetrad-FX, we plan to include several games people can play to help learn the principles of causal search to help demystify what the various algorithms are trying to accomplish.

In addition to all of the above, we would be remiss if we didn't at least mention the wide variety of projects and papers that have been made available through code or paper publications over many years, up to and including the current year--please see our list of references for a partial sample.