Purpose of SR2StanPluto.jl (v5.7)

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.

SR2StanPluto 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.

This Julia project uses Stan (the cmdstan executable) as the underlying mcmc implementation. Please see Stan.jl and/or StanSample.jl for details.

Important note

From v5 onwards the basis will no longer be StatisticalRethinking.jl but RegressionAndOtherStories.jl. Both packages have very similar content, but StatisticalRethinking.jl uses Plots.jl while RegressionAndOtherStories.jl is using (Cairo)Makie.jl.

Tagged version 4.2.0 is the last more or less complete set of scripts covering the old chapters 1 to 11.

Using Pluto's package management (or not!)

For development purposes most notebooks include a line like #Pkg.activate("~/.julie/dev/SR2StanPluto")). On the repo it is commented out. While developing code I typically run notebooks inside a project environment (by uncommenting that line).

If you frequently switch between notebooks part of this project, uncommenting (or adding) the Pkg.activate(...) line is much faster. But that requires SR2StanPluto.jl to be installed in ~/.julia/dev.

Usage

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

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

$ cd ./julia/dev
$ git clone https://github.com/StatisticalRethinkingJulia/SR2StanPluto.jl SR2StanPluto
$ cd SR2StanPluto

Or select a particular tagged version, i.e. ...//[email protected] ....

The next step assumes your basic Julia environment includes Pkg and Pluto.

2. Start a Pluto notebook server.

$ cd notebooks
$ julia

julia> using Pluto
julia> Pluto.run()

3. A Pluto page should open in a browser.

4. Select a notebook in the open a file entry box, e.g. type ./, select a notebook collection and a notebook..

Usage details

All "rethinking" data files are stored and maintained in StatisticalRethinking.jl and can be accessed via sr_datadir(...). See notebooks/00-Preface.jl for an example.

In scripts, for naming models and results of simulations I tend to use:

Models and results:

0. stan5_1 : Stan language program
1. m5_1s : The sampled StanSample model
2. q5_1s : Stan quap model (NamedTuple similar to Turing)

Draws:

3. chns5_1s : MCMCChains object (4000 samples from 4 chains)
4. part5_1s : Stan samples (Particles notation)
5. quap5_1s : Quap samples (Particles notation)
6. nt5_1s : NamedTuple with samples values
7. ka5_1s : KeyedArray object (see AxisArrays.jl)
8. da5_1s : DimArray object (see DimensionalData.jl)
9. st5_1s : StanTable 0bject (see Tables.jl)
10. i5_1s : InferenceObjects (see InferenceObjects.jl)

The default for read_samples(m1_1s) is a StanTable chains object.

Results as a DataFrame:

10. prior5_1s_df : Prior samples (DataFrame)
11. post5_1s_df : Posterior samples (DataFrame)
12. quap5_1s_df : Quap approximation to posterior samples (DataFrame)
13. pred5_1s_df : Posterior predictions (DataFrame)

The s at the end indicates Stan.

By default read_samples(m5_1s) returns a StanTable with the results. In general it is safer to specify the desired format, i.e. read_samples(m5_1s, :table) as the Julia eco-sytem is still evolving rapidly with new options.

Using read_samples(m5_1s, :...) makes it easy to convert samples to other formats.

In version 5 I expect to mainly use the output_formats :dataframe and :namedtuple.

For InferenceObjects.jl there is a separate function inferencedata(m1_1s). See the Notebook_Examples in Stan.jl for an example Pluto notebook.

Status

SR2StanPluto.jl is compatible with the 2nd edition of the book. Not all notebooks have been converted to Makie.jl yet. These are in the SR1 subdirectory. In the SR3 subdirectory I've started to look at the SR 2023 lecture examples, mainly as illustrated in the "Statistical Rethinking 2023 Python Notes".

Beginning with v5.3.0 StructuralCausalModels.jl is repaced by CausalInference.jl as an extension. To display DAGs, GraphViz.jl and CairoMakie.jl are used. This is a long term project!

ParetoSmoothedImportanceSampling.jl are included as experimental dependencies in the StatisticalRethinking.jl v3+ package.

Definitely WIP! See also below version 5 info.

Any feedback is appreciated. Please open an issue if you have questions or suggestions.

Acknowledgements

Of course, without the excellent textbook by Richard McElreath, this project 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 influenced by previous versions of StatisticalRethinking.jl, work by Karajan, Max Lapan and many other contributors.

Versions

Version 5.4.0-5.5.1

1. Further updates in using CausalInference and GraphViz.

Version 5.3

1. Switch to (Cairo)Makie.jl, Graphs.jl, CausalInference.jl, GraphViz.jl (and more probably).

Version 5

1. Version 5 is a breaking change!
2. A new look is taken at packages available in the Julia ecosystem.

Version 4

1. SR2StanPluto v4+ requires StatisticalRethinking v4+.

versions 2 & 3

1. Many additions for 2nd edition of Statistical Rethinking book.
2. Version 3 switched to using StanSample and StanQuap

Version 1

1. Initial versions (late Nov 2020).