Statistical Genetics,

Genomics & Immunity

Some examples of resources that have been helpful for my research are listed below.

This book, A First Course in Bayesian Statistical Methods, by Hoff, is designed to introduce Bayesian methods to the newcomer.

Bayesian Data Analysis, by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin, in its third edition, gives guidance to researchers interested in modeling their data using Bayesian statistical methods.

Data Analysis Using Regression and Multilevel/Hierarchical Models, by Gelman and Hill, 1st edition, provides advanced models, and a useful package, `arm` for multilevel regression.

Consider using user-friendly modeling languages such as STAN and JAGS for Bayesian statistical analysis.

Machine learning principles are illustrated in this book, Elements of Statistical Learning, by Hastie, Tibshirani and Friedman.

Explore this upcoming release, Causal Inference, by Hernan and Robbins, to begin to understand how to get beyond association and correlation.

For mathematical notation, Overleaf provides a convenient way to interactively explore LaTeX notation and document templates for mathematics, statistics, and typesetting.

Take a look at Atlassian's tutorial on how to set up a GitHub repository, or learn the workflows through a free Learn Git course at Codecademy. Also, a 2016 publication describing the basics of version control with git is available from PLOS Computational Biology.

Check out Karl Broman's tutorial on how to set up a simple site using git, GitHub, Markdown, and Jekyll.

Also, take a look at this #rstats tutorial on how to start using the R statistical programming language. There are many others available online.

For an authoritative introduction to viruses of many sorts, consult Fields Virology. If you are just getting into immunology, take a look at Janeway's Immunobiology, which also contains many good figures and background slides. Stay up to date with current infectious disease outbreaks by subscribing to ProMED-mail.