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This is the official repo for Library of Statistical Techniques (LOST) website:

LOST is a publicly-editable website with the goal of making it easy to execute statistical techniques in statistical software.

Building locally

If you are interested in local development, we use ruby 2.6.4. From there, you can run

bundle install
bundle exec jekyll serve

If you'd like to check for broken links, you can run

bundle exec jekyll build
bundle exec htmlproofer --assume-extension --allow-hash-href ./_site

Testing code samples

We have some facilities for testing to make sure that all the code samples in this repository work, at least fo the open source languages. You will need a few extra requirements for this section.

CAVEAT EMPTOR The following commands run arbitrary code samples on your machine from this repository. They are run inside isolated docker containers, but currently those containers have no ulimits. Thus, it is possible that they could, e.g., download giant files and cause your machine to come to a crawl.


You will first need to install Docker. You will also need Python 3.8 or above. After this, you will need to run the following commands:

python3 -m venv venv
source venv/bin/activate
pip install 'mistune==2.0.0rc1' 'py.test==6.1.1' 'pytest-xdist==2.1.0'

docker pull
docker pull

At this point, the docker images will not be updated unless you explicitly repull them.

Running tests

After completing the setup, you can simply run

source venv/bin/activate

Note that this will take a long time to run. You can reduce the set of tests run using the --mdpath option. For instance, to find and run all the code samples in the Time_Series and Presentation folders, you can run

py.test --mdpath Time_Series --mdpath Presentation

Furthermore, you can run tests in parallel by adding the -n parameter:

py.test -n 3 --mdpath Time_Series

Adding dependencies

The docker images in which these tests are run are managed in the lost-docker-images repo. See instructions there for adding dependencies. After which, you will need to refresh your docker images with:

docker pull
docker pull

Connecting code samples

Note that a lot of code samples in this repository are broken up by raw markdown text. If you would like to connect these in a single runtime, you should specify the language as language?example=some_id for each code sample in the chain. For instance, a Python example might be specified as python?example=seaborn as you can see in the Line Graphs Example.


Source code for the Library of Statistical Techniques






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