Purpose of StatisticalRethinkingDHMC.jl

As stated many times by the author in his online lectures, StatisticalRethinking is a hands-on course. This project is intended to assist with the hands-on aspect of learning the key ideas in StatisticalRethinking.

StatisticalRethinkingDHMC is a Julia project that uses Pluto notebooks for this purpose. Each notebook demonstrates Julia versions of code snippets and mcmc models contained in the R package "rethinking" associated with the book Statistical Rethinking by Richard McElreath.

If you prefer to work with scripts instead of notebooks, a utility in the src directory is provided (generate_scripts.jl) to create scripts from all notebooks and store those in a newly created scripts directory. Note that this is a simple tool and will override all files in the scripts directory. For exploration purposes I suggest to move some of those scripts to e.g. the research directory.

This Julia project uses Stan (the cmdstan executable) as the underlying mcmc implementation. A companion project ( StatisticalRethinkingTuring.jl ) uses Turing.jl.

Installation

To (locally) reproduce and use this project, do the following:

1. Download this project from Github and move to the downloaded directory, e.g.:

$ git clone https://github.com/StatisticalRethinkingJulia/StatisticalRethinkingDHMC.jl
$ cd StatisticalRethinkingDHMC.jl
$ julia

and in the Julia REPL:

julia> ]                                        # Actvate Pkg mode
(@v1.5) pkg> activate .                         # Activate pkg in .
(StatisticalRethinkingDHMC) pkg> instantiate    # Install in pkg environment
(StatisticalRethinkingDHMC) pkg> <delete>       # Exit package mode
julia>

If above procedure fails, if present, try to delete the Manifest.toml file and repeat above steps. As mentioned above, these steps are only needed the first time.

The next step assumes your Julia setup includes Pkg, DrWatson, Pluto and PlutoUI.

2. Start a Pluto notebook server.

$ julia

julia> using Pluto
julia> Pluto.run()

3. A Pluto page should open in a browser.

Usage

Select a notebook in the open a file entry box, e.g. type ./ and step to ./notebooks/00/clip-00-01-03s.jl. All notebooks will activate the project StatisticalRethinkingDHMC.

The data directory, in DrWatson accessible through datadir(), can be used for locally generated data, exercises, etc. All "rethinking" data files are stored and maintained in StatisticalRethinking.jl and can be accessed via sr_datadir(...). DrWatson provides several other handy shortcuts, e.g. projectdir().

A typical set of opening lines in each notebook:

using Pkg, DrWatson

# Note: Below sequence is important. First activate the project
# followed by `using` or `import` statements. Pretty much all
# scripts use StatisticalRethinking. If mcmc sampling is
# needed, it must be loaded before StatisticalRethinking:

@quickactivate "StatisticalRethinkingDHMC"
using LogDensityProblems, DynamicHMC 
using Parameters, TransformVariables
using StatisticalRethinking

# To access e.g. the Howell1.csv data file:
df = CSV.read(sr_datadir("Howell1.csv"), DataFrame)
df = df[df.age .>= 18, :]

Naming conventions

All R snippets (fragments) have been organized in clips. Each clip is a notebook.

Clips are named as clip-cc-fs-ls[s|t|d].jl where

• cc : Chapter number
• fs : First snippet in clip
• ls : Last snippet in clip
• [s|sl|t|d|m] : Mcmc flavor used (s : Stan, t : Turing)

A d is reserved for DynamicHMC models and notebooks.

The notebooks containing the clips are stored by chapter. In addition to clips, in the early notebook chapters (0-3) it is also shown how to create some of the figures in the book, e.g. Fig2.5s.jl in notebooks/chapter/02.

Special introductory notebooks have been included in notebooks/intros, e.g. intro-stan/intro-dhmc-1d.jl.

Great introductory notebooks showing Julia and statistics ( based on the Statistics with Julia book ) can be found in StatisticsWithJuliaPlutoNotebooks.

One goal for the changes in StatisticalRethinking v3 was to make it easier to compare and mix and match results from different mcmc implementations. Hence consistent naming of models and results is important. The models and the results of simulations are stored as follows:

Models:

0. stan5_1 : Stan language program
1. m5_1d : The sampled StanSample model
2. q5_1d : Stan quap model (NamedTuple similar to Turing)

Draws:

3. chns5_1d : MCMCChains object (4000 samples from 4 chains)
4. part5_1d : Stan samples (Particles notation)
5. quap5_1d : Quap samples (Particles notation)
6. nt5_1d : NamedTuple with samples values (default for read_samples(m5_1s))

Results as a DataFrame:

7. prior5_1d_df : Prior samples (DataFrame)
8. post5_1d_df : Posterior samples (DataFrame)
9. quap5_1d_df : Quap approximation to posterior samples (DataFrame) 10.pred5_1d_df : Posterior predictions (DataFrame)

The mentioned earlier, d indicates DynamicHMC.

Status

StatisticalRethinkingDHMC.jl is compatible with the 2nd edition of the book. Version 1.0.0 covers pretty much the same as StatisticalRethinking.jl v2.2.9+.

StructuralCausalModels.jl is included as en experimental dependency in the StatisticalRethinking.jl v3 package. Definitely WIP!

Two other packages created to

Any feedback is appreciated. Please open an issue.

Acknowledgements

Of course, without the excellent textbook by Richard McElreath, this package would not have been possible. The author has also been supportive of this work and gave permission to use the datasets.

This repository and format is derived from previous versions of StatisticalRethinking.jl, work by Karajan, and many other contributors.

Version 1.0.0

1. Initial version (late Nov 2020).