Supplementary Material #

On this page, I will post additional resources and supplementary material for the course.

Resources for R #

In this course, we will use R as the main computational tool. Below are some resources:

• R for Data Science by Grolemund and Wickham.
• Hands-On Programming with R by Grolemund.
• Jenny Bryan’s course on Data wrangling, exploration, and analysis with R
  • In particular, I recommend the notes on The Many Flavours of R objects
• Mine Cetinkaya’s curated list of resources for R
• If you’re looking for a deep understanding of R, I recommend Hadley Wickham’s book Advanced R
• RStudio has several great cheatsheets on their website. In particular, I recommend:
  • Data transformation
  • Data visualization
  • Rmarkdown

Other useful textbooks #

Here is a list of other textbooks that provide further (or complementary) details:

• Robert and Casella (2004). Monte Carlo Statistical Methods.
  • This book covers advanced topics related to Monte Carlo methods, with a view towards Bayesian computational methods.
• Efron and Tibshirani (1994). An Introduction to the Bootstrap.
  • This book covers many topics on jackknife and bootstrap and is a good introduction to this vast literature.
• Lange (2010). Numerical Analysis for Statisticians.
  • This book covers a wide range of topics, but in particular it discusses optimisation theory and numerical methods, and it is directly aimed at statisticians.
• Lange (2013). Optimization.
  • This book overlaps with the previous one, but it does into more depth on optimisation.