biPCPG

This package implements the Bipartite PCPG (biPCPG) algorithm¹, a generalisation of the Partial Correlation Planar Graph (PCPG) algorithm². The PCPG is a correlation-filtering method for the construction of networks intended for use on multivariate time series datasets with a single sample. The biPCPG framework generalises this approach to allows its use on similar datasets containing multi-sample multivariate time series.

The biPCPG package offers three main tools:

• Handling the dataset, via the bipcpg.utils.utils.reshape_year_matrices_to_time_series_matrices function.
• Applying the PCPG, via the bicpg.pcpg.PCPG class.
• Performing a bootstrap on the PCPG network's edges, via the bipcpg.bootstsrap.get_bootstrap_values function.

The documentation is hosted here. We recommend having a look at the tutorial to get started.

References

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1. Saenz de Pipaon Perez C, Zaccaria A, Di Matteo T. Asymmetric Relatedness from Partial Correlation. Entropy. 2022; 24(3):365. <https://doi.org/10.3390/e24030365>↩

2. Kenett DY, Tumminello M, Madi A, Gur-Gershgoren G, Mantegna RN, Ben-Jacob E. Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market. PLoS ONE. 2010; 5(12):e15032. <https://doi.org/10.1371/journal.pone.0015032>↩