A Little about resources

These are just a few additional resources on the topics that are a part of this workshop series.

R Markdown


R Markdown: The Definitive Guide for the definititve guide on all things R Markdown related. This is the most comphrensive resource for how to use R Markdown.

R Markdown Cheat Sheet for a quick overview of the basic commands/ functions in R Markdown.

Bookdown is a package that extends R Markdown that makes publishing books in nearly any format easy (epub, LaTex, html, …).

Blogdown is a package that is devoted to making static websites (Everyone should have one now…).

Document Templates

The follow packages provide nice document and even package templates for working with R Markdown

rticles for common statistical journal template (ASA, AEA, ACS, Springer, JOSS, etc)

memor for making custom memos

tufte for Tufte inspired document handbooks and templates

linl linl is not letter, a template for writing letters using R Markdown

vitae for making CVs and resumes in R Markdown

papajaavailable on github at https://github.com/crsh/papaja for making APA 6th Ed compliant documents

Sasmarkdown to facilitate using the SAS programming language in R Markdown

Helper Tools

citr is an add-in to act as a citation add in helper

tinytex is a package that will help with LaTex installations. I strongly recommend it. Follow the installation guide at https://yihui.name/tinytex/

Survey Analysis in R


Thomas Lumley’s Complex Surveys: A Guide to Analysis Using R is the book for the package upon which this workshop is based.

Shanon Loh’s Sampling: Design and Analysis is an excellent resource for survey design and inference. The results from which can be found reproduced in R in the SDaA package.


Analyze Survey Data for Free provides a compendium to different data sets and their associated designs.


survey which is the implmentation of Thomas Lumley’s book

haven for reading in SPSS/ SAS/ STATA files

foreign for reading in other files that haven might choke on.

More at the Official Statistics & Survey Methodology Taskview

Bayesian Modeling and Inference


Gelman and Hill’s Data Analysis Using Regression Modeling and Multilevel Models often called “ARM”

Gelman et al’s Bayesian Data Analysis often called “BDA”

McElreath’s Statistical Rethinking

Worked Problems

Bayesian Data Analysis


Statistical Rethinking with brms, ggplot2, and the tidyverse for Statistical Rethinking re=implmented in these packages and paradigms.

A Guide to Plotting Partial Pooling Estimators

Jim Savage’s Website for a series of great examples on using Bayesian Inference on problem solving.


This video explaining how the different sampling algorithms work (e.g. Hamiltonian, Gibbs, Metropolis Hastings)


rstan for a lower level interface with Stan where you write the Stan code and pass it to Stan via the rstan package.

rstanarm for some basic, precompiled models in Stan where come basic models have already been created and optimised in Stan and the interfaces only through R with lme4 syntax.

brms for a comphrensive interface with Bayesian modelling with lme4 syntax.

rjags for interfacing with the Jags Bayesian inference engine (Gibbs Sampling).

Advanced Analysis in R

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Michael DeWitt

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