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mathlib is a library for Scala supporting functional programming that resembles mathematical expressions such as set theory, graph theory and probability theory. This library was developed to complement the theory development method outlined in the open education book Theoretical modeling for cognitive science and psychology by Blokpoel and van Rooij (2021).

The goal of this library is to facilitate users to implement simulations of their formal theories. mathlib and Scala code is:

  • 👓 easy to read, because mathlib syntax closely resembles mathematical notation
  • ✅ easy to verify, by proving that the code exactly implements the theoretical model (or not)
  • ❤️ easy to sustain, older versions of Scala and mathlib can easily be run on newer machines

Using this library

For the scope of this documentation, we are going to assume you are familiar with setting up a blank Scala 2.13 project. Some helpful links to get you started are:

Using this library with sbt

If you have set up a Scala project with sbt, the Scala built tool, then add the following line to your build.sbt file to enable the library.

libraryDependencies += "com.markblokpoel" %% "mathlib" % "0.9.1"

Using this library with Almond

If you have set up a Scala project in jupyter notebooks using Almond you can enable the library by adding the following line to a worksheet.

import $ivy.`com.markblokpoel::mathlib:0.9.1`

Import mathlib packages

After enabling mathlib in your project you most likely will want to use some of its components. See the development section below for the available components and their import statements. Adding these import statements to a .scala file will import the component and allow you to write code using it. The examples below show how this is done.


The Github repository contains a few demos that can be found under mathlib/demos. To run and play around with these demos there are two options.

Option 1 (clean project): First follow the instructions above for setting up this library. Then, download the scala files from the repository and copy them into your Scala project. They can be run as any Scala application, e.g., run Scala applications in Intellij

Option 2 (clone repository): This option will download the full mathlib source code and assumes that git, Scala and SBT have been installed (see installation instructions for Scala and SBT here and for git here). First, clone the GitHub repository:

$ git clone

Open a terminal and cd into the repository root folder (usually called mathlib):

$ cd mathlib

Run a demo using the following command, runCoherenceDemo can be replaced by runVertexCoverDemo, runGraphsDemo, or runSubsetChoiceDemo:

$ sbt runCoherenceDemo

Tutorials (work in progress)

You can find extensive tutorials on the basics of Scala and using mathlib in the markblokpoel/mathlib-examples GitHub repository. The tutorials include a link to an online service (binder) where you can try out the library without needing to install anything.


The current version of mathlib is v0.9.0 and the following components are complete, fully usable and will remain backwards compatible (i.e., code that uses these components will still run with future versions of mathlib). Currently, this library only supports Scala 2 and not Scala 3.

Component import statement Minimum mathlib and Scala versions
Set theory import mathlib.set.SetTheory._ v0.9.0 / 2.13.9
Graph theory import mathlib.graph._
import mathlib.graph.GraphImplicits._
v0.9.0 / 2.13.9


The following features are currently under development or planned. Any existing code related to these features may undergo significant changes in future versions. It may also contain bugs and code based on these features may not work with future versions of mathlib.

Feature Priority Description Completion
Probability revision High Rewrite probability implementation to support distributions with an arbitrary number of conditionals and high precision numbers. Started initial exploration.
Scala 3 support Medium Port the library to Scala 3 and support backwards compatibility. Not started.
Utilities Medium Explore utility functions (e.g., bounded search tree) and write documentation Initial draft.
Graph theory: Hyper graphs Low Implement basic graph algorithms for hyper graphs. Not started.
Graph theory: Trees Low Implement tree graph types and algorithms. Not started.
Set theory Complete implementation and documentation. Completed in v0.9.0
Graph theory Complete implementation and documentation for non-hyper graphs. Completed in v0.9.0


This software is licensed under the GNU General Public License v3.0.

About this project


Mark Blokpoel, @markblokpoel,,


Thanks for considering contributing to this project. We welcome all contributions and feedback, your help is essential for keeping it great.

Contributions can be made by providing feedback, requests or bug reports through issues or by providing updated / extended code via a pull request (see below). Please read our Code of Conduct before participating in this community.

Content contributions to this project are released to the public under the project's open source license. Whenever you add Content to a repository containing notice of a license, you license that Content under the same terms, and you agree that you have the right to license that Content under those terms. If you have a separate agreement to license that Content under different terms, such as a contributor license agreement, that agreement will supersede.

Authorship and credit

If you contribute to this work, such as by a pull request (PR), please also add yourself to the author list in file in the same PR, ideally with your real name, your GitHub username, and your ORCID.

If you use this work, please credit/cite it and the authors.

Submitting a pull request

  1. Fork and clone the repository
  2. Create a new branch: git checkout -b my-branch-name
  3. Make your change
  4. Push to your fork and submit a pull request
  5. Pat yourself on the back and wait for your pull request to be reviewed and merged.


We thank the Computational Cognitive Science group at the Donders Center for Cognition (Nijmegen, The Netherlands) for useful discussions and feedback. A special thanks to Laura van de Braak, Olivia Guest and Iris van Rooij whose conceptual contributions and support have been invaluable. We further thank Max Hinne for helpful pointers on random graph generation.

This project was supported by Netherlands Organization for Scientific Research Gravitation Grant of the Language in Interaction consortium 024.001.006, the Radboud School for Artificial Intelligence and the Donders Institute, Donders Center for Cognition.


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