Applied Longitudinal Data Analysis in brms and the tidyverse

This project is based on Singer and Willett's classic (2003) text, Applied longitudinal data analysis: Modeling change and event occurrence. You can download the data used in the text at http://www.bristol.ac.uk/cmm/learning/support/singer-willett.html and find a wealth of ideas on how to fit the models in the text at https://stats.idre.ucla.edu/other/examples/alda/. My contributions show how to fit these models and others like them within a Bayesian framework. I make extensive use of Paul Bürkner's brms package, which makes it easy to fit Bayesian regression models in R using Hamiltonian Monte Carlo (HMC) via the Stan probabilistic programming language. Much of the data wrangling and plotting code is done with packages connected to the tidyverse.

You can find drafts of several of the chapters available as an online book at https://bookdown.org/content/4253/. Files with drafts included in the online book are listed in the root directory of this GitHub repository. You can find the .Rmd files for chapters under development in the in_the_works folder.

There is no anticipated completion data for this project. I’ll update the ebook as new chapters get completed and/or I find reason to make major revisions to those already listed in the root directory. Regardless of the phase of the project, helpful suggestions are welcome. Feel free to open an issue or make a pull request.

Reference

Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY, US: Oxford University Press.