Useful links
Course resources
 Tips on writing Rmd reports
 Tips on writing peer reviews
 A page of example datasets.
 How to use
rstan
on the cluster  How to check if
rstan
is installed correctly  A description of how to use git to get the course material.
 A primer on linear algebra
 Some notes on how the slides are made from Rmd.
Tutorials:
 An example of debugging Stan convergence
 A technical look at brms
Stan/brms
 Stan documentation
 the Reference Manual describes the syntax and workings of a Stan program
 the Functions Reference is where you look up “what’s that function again?”
 the User’s Guide has examples of complex models implemented in Stan, and discusses good programming practice
 RStan documentation
 Example models in Stan: each contains a Stan program, code for simulating data, real data, and model output and diagnostics
 Vignette on stanfit objects
 Brief guide to Stan’s warnings
 Rewrite of Kruschke’s models using brms (and tidyverse), by A. Solomon Kurz.
 Rewrite of Applied longitudinal data analysis using brms (and tidyverse) by A. Solomon Kurz
Other books
If you are looking for more reference or reading material, these are also good:

Bruce, Bruce, and Gedeck, Practical Statistics for Data Scientists. O’Reilley Publishers. code repository and link to the book

Logan, M. 2010. Biostatistical Design and Analysis Using R. WileyBlackwell. A fairly comprehensive book that covers how to use R to do many of the topics in Quinn and Keough.

Wickham, H. & G. Grolemund. 2016. R for Data Science. O’Reilly Publishers. (free web version) How to do many common data analysis tasks in R, specifically in the tidyverse.

Wickham, H. ggplot2: Elegant Graphics for Data Analysis, 2nd edition (free web version of the inprocess 3rd edition) A more comprehensive reference to ggplot2 than the chapter of R for data science. Also see the documentation.

Wilke, Claus O. Fundamentals of Data Visualization.. O’Reilly Publishers. (free web version) How to think about visualization (with source code for plots available!).

Haddock, S. and C. Dunn. 2011. Practical Computing for Biologists. Sinauer and Associates.
Miscellaneous R tips
 knitr chunk options for control of Rmarkdown code chunks
 Formulae in R and, in more detail, General linear models in R FAQ
 How to print the source code
for functions that don’t show it to you when you type their names. (tldr;
showMethods(fun); getMethod(fun, c(x='class1', y='class2'))
)
ggplotting
 ggplot2 quick reference
 practical ggplot2 an annotated website of examples by Claus Wilke
Rstudio
 Strongly recommended global configuration:
General resources
 HandsOn Programming with R, by Garrett Grolemund
 linuxcommand.org and bashguide
 Software Carpentry
 Reproducible Research by Karl Broman: talk and course
 Karl Broman’s excellent short tutorials on rmarkdown, git/github, make, perl, and more.
 a visual introduction to git
 Bioinformatics Data Skills by Vince Buffalo
 Jenny Bryan’s stat 545: Data wrangling, exploration, and analysis with R
 Yaniv Brandvain’s Applied Biostats course
Probability and statistics
 List of common probability distributions
 ANOVA: a short intro using R by Lukas Meier
 Interactive plot of the beta distribution
 Interactive plot of the gamma distribution
 Interactive plot of Student’s tdistribution