qumin 4.0

Qumin: QUantitative Modelling of INflection

Qumin (QUantitative Modelling of INflection) is a package for the computational modelling of the inflectional morphology of languages. It was initially developed for Sacha Beniamine’s PhD dissertation.

Contributors: Sacha Beniamine, Jules Bouton.

Documentation: https://qumin.readthedocs.io/

Pypi: https://pypi.org/project/qumin/

Gitlab: https://gitlab.com/qumin/Qumin

Warning

The Github repo has now been migrated to gitlab. Update your bookmarks !

The current version was significantly updated since the publications cited below. These updates do not affect results, and focused on bugfixes, command line interface, paralex compatibility, workflow improvement and overall tidyness.

For more detail, you can refer to Sacha’s dissertation (in French, Beniamine 2018).

Quick Start

To install Qumin, run:

pip install qumin

To compute patterns and entropies:

qumin action=pred data=<path/to/paralex.package.json>

To compute patterns and a lattice of microclasses:

qumin action=lattice data=<path/to/paralex.package.json>

For a detailed introduction, you can head to the tutorials. More advance use-cases are handled in the how-to guides. The full reference of command line options is provided with examples:

• Common options

• Lattices

• Macroclasses

• Microclasses

• Patterns

• Predictability heatmaps

• Predictability

An API reference is also available for using Qumín functions in python scripts.

Citing

If you use Qumin in your research, please cite the Qumin Zenodo deposit as well as the relevant paper for the specific actions used (see below). Substitute “<version used>” with the version of Qumin you used.

• Beniamine & Bouton. (2025). Qumin (v3.<version used>). [Python package]. doi: 10.5281/zenodo.15008373 https://pypi.org/project/qumin/

• For alternation patterns, please cite Beniamine (2018).

• For entropy calculations, please also cite Bonami and Beniamine (2016).

• For advanced predictibility calculations using probability of success or token frequencies, please also cite Bouton and Bonami (2026, in press).

• For macroclass inference, please also cite Beniamine, Bonami and Sagot (2018)

• For lattice inference, please also cite Beniamine (2021).

For reproducibility, mentioning the exact current revision is important.

To appear in the publications list, send Sacha an email with the reference of your publication at s.<last name>@surrey.ac.uk