Welcome to utlvce’s documentation!

The utlvce package is a Python implementation of the UT-LVCE algorithms from the 2022 paper “Perturbations and Causality in Gaussian Latent Variable Models”, by A. Taeb, JL. Gamella, C. Heinze-Deml and P. Bühlmann.

You can find the source code in the package’s GitHub repository. The code to reproduce the experiments and figures in the paper can be found in a separate repository.

Installation

You can install the latest version of the package using pip, i.e.

pip install utlvce

How to run the UT-LVCE algorithms

If you’re interested in running the algorithms proposed in the paper for your own work, you can find details on how to do so and examples under the section Running the UT-LVCE algorithms.

This page also documents the other functionalities of the package, such as generating synthetic data and random models for your own experiments (see utlvce.generators and utlvce.model). If you’re interested in the alternating optimization procedure from section 4.1, you can find more details under utlvce.score.

Versioning

The pacakge is still at its infancy and its API may change in the future. Non backward-compatible changes to the API are reflected by a change to the minor or major version number, e.g.

code written using utlvce==0.1.2 will run with utlvce==0.1.3, but may not run with utlvce==0.2.0.

License

The code is open-source and shared under a BSD 3-Clause License. You can find the full license and the source code in the GitHub repository.

Feedback

Feedback is most welcome! You can add an issue in the repository or send an email.