# 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 release contains drafts of Chapters 1 through 6 and 9 through 13. Chapters 1 through 6 provide the motivation and foundational principles for fitting longitudinal multilevel models. Chapters 9 through 13 motivation and foundational principles for fitting discrete-time survival analyses.

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.

## R setup

To get the full benefit from this ebook, you’ll need some software. Happily, everything will be free (provided you have access to a decent personal computer and an good internet connection).

First, you’ll need to install R, which you can learn about at https://cran.r-project.org/.

Though not necessary, your R experience might be more enjoyable if done through the free RStudio interface, which you can learn about at https://rstudio.com/products/rstudio/.

Once you have installed R, execute the following to install the bulk of the add-on packages. This will probably take a few minutes to finish. Go make yourself a coffee.

packages <- c("bayesplot", "brms", "broom", "devtools", "flextable", "GGally", "ggmcmc", "ggrepel", "gtools", "loo", "patchwork", "psych", "Rcpp", "remotes", "rstan", "StanHeaders", "survival", "tidybayes", "tidyverse")

install.packages(packages, dependencies = T)

A few of the other packages are not officially available via the Comprehensive R Archive Network (CRAN; https://cran.r-project.org/). You can download them directly from GitHub by executing the following.

devtools::install_github("stan-dev/cmdstanr")
remotes::install_github("stan-dev/posterior")
devtools::install_github("rmcelreath/rethinking")

It’s possible you’ll have problems installing some of these packages. Here are some likely suspects and where you can find help:

@book{kurzAppliedLongitudinalDataAnalysis2021,
}