References

This chapter contains links to resources we’ve found helpful as we’ve learned and used Julia.

Documentation & Tutorials

• The Official Julia Documentation

  • Noteworthy differences from R, from Python, and from MATLAB

  • Integers and floating point numbers

• The DataFrames.jl Documentation

• Interactive Julia Data Frames Tutorial

• UCLA’s Biostatistics 257 Course

  • 2022 Materials

  • 2023 Materials

Communities

• The Official Julia Discourse

• University of California Julia User Group

Editors

• Pluto.jl, reactive notebooks for Julia

• Jupyter, notebooks for Julia, Python, text, and R

• Julia for VSCode

Packages

• Revise.jl, a package to watch and automatically reload Julia scripts when they change. Useful for testing code as you write it.

• Literate.jl for literate programming—generate formatted documents from comments in a Julia source file.

• Statistical modeling

  • Distributions.jl provides sampling functions and density/distribution functions for common probability distributions.

  • Flux.jl provides native Julia support for statistical modeling and machine learning. Some models are GPU-accelerated.

  • Turing.jl provides probabilistic programming and Bayesian modeling functions.

• Unitful.jl to assign low- or no-overhead units to values and automatically perform appropriate conversions.

• JuMP.jl, a domain-specific language for mathematical optimization in Julia.

• CUDA.jl for low-level GPU computing support.

• Symbolics.jl, a computer algebra system for Julia.

• Genie, a web framework similar to Django.