Research resources

Here is a list of helpful papers and other resources for getting started working with me.

Reading papers

Reading scientific papers can be hard! Here are a couple of resources for how to prioritize your read-throughs of papers (hint: you shouldn't necessarily just read the paper straight through the first time!).

• The Leek group guide to reading papers, a fairly comprehensive guide with lots of places to start reading
• S. Keshav's guide to reading papers, which I have found especially helpful for conference and arXiv papers

I suggest getting started by setting a daily alert on arXiv, an open-access archive for scholarly articles; I personally have an alert set for the following categories: stat.CO (computation), stat.ME (methodology), stat.ML (machine learning), stat.TH (statistics theory). You can also set up alerts on bioRxiv and medRxiv.

Background on (generalized) linear regression

• Chapter 1 of Biostat 311, taught at the University of Washington in 2018 by myself and Kelsey Grinde. These slides cover univariate linear regression.
• Chapter 2 of Biostat 311. These slides cover multivariate linear regression.
• Chapter 3 of Biostat 311. These slides cover generalized linear regression.

Background on penalized regression

• ridge regression
• the lasso: pairs a sparsity-inducing penalty with a least-squares loss function, and is widely used
• more to come...

Background on more flexible regression

• classification and regression trees: a simple-yet-flexible approach to regression
• generalized additive models
• random forests: build on regression/classification trees by building forests of multiple (bagged) trees
• Super Learner: combine the predictions from multiple candidate learners together to make better predictions
• more to come...

Background on nonparametric and robust statistics

• more to come...

Background on software

Learning R

• https://education.rstudio.com/learn/
• https://rstudio.cloud/learn/primers
• https://swirlstats.com/
• https://adv-r.hadley.nz/ (for a bit more advanced treatment)
• https://r4ds.had.co.nz/ (for more of a data-science-y treatment)

Learning Git and GitHub

• https://happygitwithr.com/
• https://git-scm.com/book/en/v2/ (especially this section, which talks about configuring programs for commit message editing and templates)
• Tips for git commit messages
• Tips for how often to commit

Data and packages

• Tidy data, a nice framework for organizing datasets described by Hadley Wickham
• The Leek group guide to data sharing, a nice framework for organizing data that you are working on
• The Leek group guide to creating R packages (has links to other resources as well)