covdepGE-package | R Documentation |
A covariate-dependent approach to Gaussian graphical modeling as described in Dasgupta et al. (2022). Employs a novel weighted pseudo-likelihood approach to model the conditional dependence structure of data as a continuous function of an extraneous covariate. The main function, covdepGE::covdepGE(), estimates a graphical representation of the conditional dependence structure via a block mean-field variational approximation, while several auxiliary functions (inclusionCurve(), matViz(), and plot.covdepGE()) are included for visualizing the resulting estimates.
Maintainer: Jacob Helwig jacob.a.helwig@tamu.edu
Authors:
Sutanoy Dasgupta sutanoy@stat.tamu.edu
Peng Zhao pzhao@udel.edu
Bani Mallick bmallick@stat.tamu.edu
Debdeep Pati debdeep@stat.tamu.edu
(1) Sutanoy Dasgupta, Peng Zhao, Jacob Helwig, Prasenjit Ghosh, Debdeep Pati, and Bani Mallick. An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling. arXiv preprint, 1–64, 2023.
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