Stan V8

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Purpose

A collection of examples demonstrating the use Stan's cmdstan (as an external program) from Julia.

Background info

The first 2 generations of Stan.jl took a similar approach as the recently released CmdStanR and CmdStanPy options to use Stan's cmdstan executable.

Stan.jl v7.x constitutes the third generation and covers all of cmdstan's methods in separate packages, i.e. StanSample.jl, StanOptimize.jl, .jl, etc., including an option to run generate_quantities. In a sense, it extends Tamas Papp's approach taken in StanRun, StanDump and StanSamples.

Stan.jl v8.0 is based on StanSample.jl v5, StanOptimize.jl v3 and StanQuap.jl v2.

Requirements

Stan's cmdstan executable needs to be installed separatedly. Please see cmdstan installation.

Note: StanSample.jl v5.3, supports multithreading in the cmdstan binary and requires cmdstan v2.28.2 and up. To activate multithreading in cmdstan this needs to be specified during the build process of cmdstan.

Once multithreading is included in cmdstan, set the num_threads in the call to stan_sample, e.g. rc = stan_sample(sm; data, num_threads=3, num_chains=2, seed=-1)

For more info on Stan, please go to http://mc-stan.org.

Conda based installation walkthrough for running Stan from Julia on Windows

Note: The conda way of installing also works on other platforms. See also.

Make sure you have conda installed on your system and available from the command line (you can use the conda version that comes with Conda.jl or install your own).

Activate the conda environment into which you want to install cmdstan (e.g. run conda activate stan-env from the command line) or create a new environment (conda create --name stan-env) and then activate it.

Install cmdstan into the active conda environment by running conda install -c conda-forge cmdstan.

You can check that cmdstan, g++, and mingw32-make are installed properly by running conda list cmdstan, g++ --version and mingw32-make --version, respectively, from the activated conda environment.

Start a Julia session from the conda environment in which cmdstan has been installed (this is necessary for the cmdstan installation and the tools to be found).

Add the StanSample.jl package by running ] add StanSample from the REPL.

Set the CMDSTAN environment variable so that Julia can find the cmdstan installation, e.g. from the Julia REPL do: ENV["CMDSTAN"] = "C:/Users/Jakob/.julia/conda/3/envs/stan-env/Library/bin/cmdstan" This needs to be set before you load the StanSample package by e.g. using it. You can add this line to your startup.jl file so that you don't have to run it again in every fresh Julia session.

Versions

Version 8.1.0

  1. Support StanSanple.jl v5.3 multithreading in cmdstan

Version 8.0.0

  1. Supports both CMDSTAN and JULIA_CMDSTAN_HOME environment variables to point to the cmdstan installation.
  2. Thanks to @jfb-h completed testing with using conda to install cmdstan
  3. Refactored code between StanBase.jl and the other StanJulia packages.

Version 7.1.1

  1. Doc fixes by Jeremiah P S Lewis.
  2. Switch default output_format for read_samples() to :table.
  3. Add block extract for DataFrames, e.g. DataFrame(m1_1s, :log_lik)

Version 7.1.0

  1. Doc fixes. Prepare for switching default output_format for read_samples() to :table.

Version 7.0

This is a breaking update!

  1. Use KeyedArray chains as default output format returned by read_samples.
  2. Drop the output_format keyword argument in favor of a regulare argument.
  3. Removed mostly outdated cluster and thread based examples.
  4. Added a new package DiffEqBayesStan.jl.