stancon2018_intro

Materials for the StanCon 2018 Intro class for both R and Python users (thanks to @amaloney). See below for how to install an environment that can run the notebooks.

Other resources:

• See the reading-list.html or reading-list.pdf in this repo
• Aki Vehtari's model selection tutorial from StanCon 2018: https://github.com/avehtari/modelselection_tutorial
• Bob Carpenter's slides on MCMC convergence from StanCon 2018: http://mc-stan.org/workshops/stancon2018_intro/diagnosing-convergence.pdf

Installation (macOS using brew)

We will be using conda to facilitate the creation of virtual environments, and the handling of dependencies used in the introduction class. Below outlines how to create an environment that will run notebooks for Jupyter or RStudio on a macOS system.

1. Install brew if you have not already done so. We will be using it as our package manager for installing miniconda. If you do not want to install brew on your system, then follow the instructions on Anaconda for how to install miniconda on your system.

2. Install miniconda and update your PATH environment variable so that you have access to the newly installed conda package manager. conda is different than brew in that conda can create virtual environments that are segregated from your system environment.

   1. brew install miniconda
   2. Update your path by adding export PATH=/usr/local/miniconda3/bin:$PATH to the end of your ~/.bashrc or ~/.bash_profile file, and then sourcing it.

3. Clone the StanCon2018 Intro repository someplace on your machine and change directories into it.

   1. git clone https://github.com/jgabry/stancon2018_intro
   2. cd stancon2018_intro

4. Next we will install the conda virtual environment, which includes several packages: RStudio, Jupyter, a Python3 kernel for Jupyter, and an R kernel for Jupyter. Once the dependencies have been installed, we will need to activate the new virtual environment, so that we can access the newly installed packages.

   1. conda env create --file environment.yml
   2. Activate the new environment with source activate StanCon2018_Intro.

5. Start the notebook environment you are familiar with.

   1. Jupyter

      1. jupyter notebook
      2. Your default browser should navigate you to a page with the folder structure of this repository. Select the jupyter_notebooks folder and open either the StanCon2018 Intro-Python3.ipynb or the StanCon2018 Intro-R.ipynb notebook. The Python3 notebook should start with the Python3 kernel, while the R notebook should start with the R kernel.

   2. RStudio

      1. rstudio
      2. This will open the familiar RStudio platform.