Thank you so much Abby ❤️🥹

Thank you so much family! Hope you’re doing well!!

🎓 Paper: osf.io/preprints/os...
📺 YouTube Tutorials: youtube.com/playlist?lis...
📦 GitHub: github.com/jedoriscar/U...

#RStats #MachineLearning #SocialPsych #AcademicTwitter #DataScience

This paper was a labor of love and I’m so grateful to my brilliant co-authors Michalis Mamakos, @sylviapperry.bsky.social , and Tessa Charlesworth for their support, feedback, and generosity throughout the process.

We also include extensive annotated R code, visual walkthroughs, and a free YouTube tutorial series to make this genuinely hands-on. If you’re curious about how UML can help you uncover new patterns in your data—or just want to brush up on clustering and dimension reduction—check it out!

Each section walks through:

- what the method is

- how to implement it in R

- how to interpret the results

- real examples using Project Implicit data

We focus on 4 key UML techniques:

- K-means clustering

- DBSCAN

- Principal Component Analysis (PCA)

- Market Basket Analysis (MBA)

Social cognition research is drowning in big, complex data—but we often use the same familiar methods. Unsupervised Machine Learning (UML) gives us tools to explore patterns without predefined outcomes. We wrote this paper to demystify UML and help researchers use it meaningfully.

🧵 A brief thread on what this tutorial paper is about & why I’m excited about it:

Let’s gooooo! Dr. Vallabha has a nice ring to it!

I would like to share that the family requests donations be made to the Innocence Project (innocenceproject.org/donate/) or the American Civil Liberties Union (action.aclu.org/give/now) in lieu of flowers. 💖💐