HOI: A Python toolbox for high-performance estimation of Higher-Order Interactions from multivariate data - Archive ouverte HAL
Article Dans Une Revue Journal of Open Source Software Année : 2024

HOI: A Python toolbox for high-performance estimation of Higher-Order Interactions from multivariate data

Matteo Neri
Dishie Vinchhi
  • Fonction : Auteur
Christian Ferreyra
  • Fonction : Auteur
Thomas Robiglio
  • Fonction : Auteur
Onur Ates
  • Fonction : Auteur
Marlis Ontivero-Ortega
Andrea Brovelli
Marlis Ontivero-Ortega
Etienne Combrisson

Résumé

The HOI toolbox provides easy-to-use information theoretical metrics to estimate pairwise and higher-order information from multivariate data. The toolbox contains cutting-edge methods, along with core entropy and mutual information functions, which serve as building blocks for all metrics. In this way, HOI is accessible both to scientists with basic Python knowledge using pre-implemented functions and to experts who wish to develop new metrics on top of the core functions. Moreover, the toolbox supports computation on CPUs and GPUs. Finally, HOI provides tools for visualizing and presenting results to simplify the interpretation and analysis of the outputs.

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hal-04778108 , version 1 (12-11-2024)

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Matteo Neri, Dishie Vinchhi, Christian Ferreyra, Thomas Robiglio, Onur Ates, et al.. HOI: A Python toolbox for high-performance estimation of Higher-Order Interactions from multivariate data. Journal of Open Source Software, 2024, 9 (103), pp.7360. ⟨10.21105/joss.07360⟩. ⟨hal-04778108⟩
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