Skip to content


Julia is a high-performance language for scientific computing, which also happens to be readable. Julia may be the best choice for our work, though there may be times when your collaborators want to use Python or R, or when tools already exist in those languages that are perfect for a particular project.


Use the current stable release of Julia.


If you’re coming from MATLAB or Python, here is a useful cheatsheet for translating common operations into Julia.

Development Environments

Juno is a Julia IDE built off the Atom text editor.

  • Turing: probabilistic programs for statistical inference.
  • Flux: machine learning libraries

David Anthoff at UC Berkeley has been developing the Mimi Framework for integrated assessment modeling. Many IAMs have been ported into this framework, and can be coupled with earth-system models.

Learn More

A good introduction to Julia is Introduction to Computational Thinking, an online course from MIT.

Last update: February 16, 2022