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:
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.