Dual PC (dualPC)

Module name	dualpc
Package	dualPC
Version
Language	R
Docs
Paper	Giudice et al.[1]
Graph type	CG, CPDAG
MCMC	No
Edge constraints	No
Data type	C
Data missingness
Intervention type
Docker	bpimages/dualpc:585751b

Dual PC

The dual PC algorithm is a novel scheme to carry out the conditional independence tests within the PC algorithm for Gaussian data, by leveraging the inverse relationship between covariance and precision matrices. The algorithm exploits block matrix inversions on the covariance and precision matrices to simultaneously perform tests on partial correlations of complementary (or dual) conditioning sets. Simulation studies indicate that the dual PC algorithm outperforms the classic PC algorithm both in terms of run time and in recovering the underlying network structure.

Example JSON

[
  {
    "id": "dualpc",
    "alpha": [
      0.001,
      0.01,
      0.05,
      0.1
    ],
    "skeleton": false,
    "max_ord": null,
    "timeout": null
  }
]

[1]

Enrico Giudice, Jack Kuipers, and Giusi Moffa. The dual pc algorithm for structure learning. In Antonio Salmerón and Rafael Rumı́, editors, Proceedings of The 11th International Conference on Probabilistic Graphical Models, volume 186 of Proceedings of Machine Learning Research, 301–312. PMLR, 05–07 Oct 2022. URL: https://proceedings.mlr.press/v186/giudice22a.html.