What and why

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.

Caution: Work in progress

This inaugural 0.0.1 release contains first drafts of Chapters 1 through 5 and 9 through 12. Chapters 1 through 5 provide the motivation and foundational principles for fitting longitudinal multilevel models. Chapters 9 through 12 motivation and foundational principles for fitting discrete-time survival analyses. A few of the remaining chapters have partially completed drafts and will be added sometime soon.

In addition to fleshing out more of the chapters, I plan to add more goodies like introductions to multivariate longitudinal models and mixed-effect location and scale models. But there is no time-table for this project. To keep up with the latest changes, check in at the GitHub repository, https://github.com/ASKurz/Applied-Longitudinal-Data-Analysis-with-brms-and-the-tidyverse, or follow my announcements on twitter at https://twitter.com/SolomonKurz.