Overview#
How to install this package and run the first calculation.
Start your endeavour here!
Idea, structure and theoretical background of delaynet.
Explore the details here!
A collection of short demos showcasing the capabilities of this package.
What is delaynet?#
delaynet is a Python package designed to facilitate the reconstruction and analysis of
delay functional networks from time series. It provides tools for data preparation and
detrending, computing multiple connectivity measures (e.g. Granger causality, transfer
entropy, correlations), reconstructing networks with optimal lag selection, and
analysing network topology.
More abstractly, it applies to any system with information propagation where delays
play a role.
Setup and use#
To set up delaynet, see the Getting Started page, more on
the details of the inner workings can be found on the
Reference pages.
Furthermore, you can also find the API documentation.
How to cite#
If you use delaynet in your research, find the CITATION.cff file
in the repository and cite it
accordingly.
GitLab provides citation metadata from the CITATION.cff, and you can also copy an APA
or BibTeX entry from the repository.
A preprint is being prepared and will be submitted; arXiv/DOI links will be added upon availability.
Contributing#
If you want to contribute to the development of delaynet, please read the
CONTRIBUTING.md
file.
Acknowledgments#
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 851255). This work was partially supported by the María de Maeztu project CEX2021-001164-M funded by the MICIU/AEI/10.13039/501100011033 and FEDER, EU.
