biPCPG

This package implements the Bipartite PCPG (biPCPG) algorithm 1, a generalisation of the Partial Correlation Planar Graph (PCPG) algorithm 2. 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 reshape_year_matrices_to_time_series_matrices() function.

• Applying the PCPG, via the PCPG class.

• Performing a bootstrap on the PCPG network’s edges, via the get_bootstrap_values() function.

We recommend having a look at the tutorial to get started.

References

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 (2010) Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market. PLoS ONE 5(12): e15032. <https://doi.org/10.1371/journal.pone.0015032>