Skip to content

INSTALL.md

Latest commit

 

History

History
41 lines (31 loc) · 3.21 KB

INSTALL.md

File metadata and controls

41 lines (31 loc) · 3.21 KB

Installation Guide

To utilize the artifacts in this repository, follow the installation guides below.

  • To view .md Markdown files contained in this repository, consider a Markdown viewer like the one integrated into Visual Studio Code.
  • To display and edit graphs specified in .graphml format, use a graph editor like yEd by yworks.

Python

To execute the Python source code, make sure that you have Python 3.10.0 or any later version installed. Make sure that the installation of Python also includes the package manager pip. Then, execute the following steps:

  1. Optionally, to isolate your local python environment from this one, create a virtual environment:
    1. Execute python -m venv .venv to create a virtual environment in the directory .venv/.
    2. Activate the virtual environment, e.g., via .venv/scripts/Activate.ps1 on Windows.
  2. Install all necessary requirements via pip install -r requirements.txt.
  3. When using a virtual environment and wanting to execute jupyter notebooks (.ipynb), you also need to install a jupyter kernel via ipython kernel install --user --name=venv. IDEs like VS Code will prompt to install the kernel automatically

R

In order to run the .R scripts and .Rmd notebooks, ensure that you have R (version > 4.0) and an appropriate IDE like RStudio installed on your machine. Then, install all required packages via install.packages(c("tidyverse", "patchwork")).

R for Bayesian Data Analysis

In order to run the .R scripts and .Rmd notebooks, ensure that you have R (version > 4.0) and an appropriate IDE like RStudio installed on your machine. Then, ensure the following steps:

  1. Install the C toolchain by following the instructions for Windows, Mac OS, or Linux respectively.
  2. Restart RStudio and follow the instructions starting with the Installation of RStan
  3. Install the devtools and the latest version of stan by running the following commands
    install.packages("devtools")
    devtools::install_github("stan-dev/cmdstanr")
    cmdstanr::install_cmdstan()
  1. Install all required packages via install.packages(c("tidyverse", "ggdag", "brms", "marginaleffects", "patchwork")).
  2. Create a folder called fits within src/ such that brms has a location to place all Bayesian models.
  3. Open the .Rproj file with RStudio which will setup the environment correctly.