Installation of Julia

Julia is a high-level and interactive programming language (like R or Matlab), but it is also high-performance (like C). To install Julia, follow instructions here. For a quick & basic tutorial on Julia, see learn x in y minutes.


  • Visual Studio Code provides an editor and an integrated development environment (IDE) for Julia: highly recommended!
  • You can also run Julia within a Jupyter notebook (formerly IPython notebook).

IMPORTANT: Julia code is just-in-time compiled. This means that the first time you run a function, it will be compiled at that moment. So, please be patient! Future calls to the function will be much much faster. Trying out toy examples for the first calls is a good idea.

Installation of the BayesianNetworkRegression.jl package

To install the package,

  1. Clone BayesianNetworkRegression.jl to your local machine.
  2. Type inside Julia:
dev PATH/BayesianNetworkRegression.jl

where PATH is the path to the BayesianNetworkRegression.jl depository on your machine.

The first step can take a few minutes, be patient.

The BayesianNetworkRegression.jl package has dependencies like Distributions and DataFrames (see the Project.toml file for the full list), but everything is installed automatically.

Loading the Package

To check that your installation worked, type this in Julia to load the package. This is something to type every time you start a Julia session:

using BayesianNetworkRegression

This step can also take a while, if Julia needs to pre-compile the code (after a package update for instance).

Press ? inside Julia to switch to help mode, followed by the name of a function (or type) to get more details about it.