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

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…).

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

`papaja`

available 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

`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/

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

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*

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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