I’m not a statistician and I have no formal background in computer science. But I met a great statistics mentor in grad school who was enthusiastic, knowledgeable, and very generous with his time. In one of his stats electives, we used Hayes’s first edition text and I learned a lot in that semester.
Yet a large portion of my training has been out of the classroom, working with messy real-world data, and looking online for help. One of the great resources I happened on was idre, the UCLA Institute for Digital Education. They perform a variety of services, but I benefited the most from was their portfolio of richly annotated textbook examples. Their online tutorials are among the earliest inspirations for this project. More so than my old statistics seminar lecture notes, high-quality and freely-available resources like this are where most of my day-to-day data analysis skills come from. We need more resources like this.
Hayes’s work has become influential in many corners of the academy, including my own—psychology. His PROCESS macro has been aimed at SPSS and SAS users, which is understandable given their popularity in the social sciences. But over the past few years, I’ve moved away from proprietary programs like SPSS to R. Not only is R free and open source, but I find it a more flexible and useful tool for data analysis. In fairness, Hayes expanded his second edition to include R code, which is a great move forward. But his work is done from a frequentist OLS perspective and there have been a lot of exciting developments in the world of applied Bayesian statistics. If you’re an R user and want to learn about Bayesian data analysis, I think Bürkner’s brms is the best package around. It’s flexible, uses reasonably-approachable syntax, has sensible defaults, and offers a wide array of post-processing convenience functions. In addition, the R code in Hayes’s second edition does not leverage the power of the tidyverse. The purpose of this project is to connect Hayes’s insights into regression with the Bayesian paradigm. We’ll do so within the free and open-source R ecosystem, highlighting the Bayesian brms package, and using functions from the tidyverse to streamline our code.