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covdepGE: Covariate Dependent Graph Estimation
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.
Getting started
Browse package contents
Package details |
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| Author | Jacob Helwig [cre, aut], Sutanoy Dasgupta [aut], Peng Zhao [aut], Bani Mallick [aut], Debdeep Pati [aut] |
| Maintainer | Jacob Helwig <jacob.a.helwig@tamu.edu> |
| License | GPL (>= 3) |
| Version | 1.0.1 |
| URL | https://github.com/JacobHelwig/covdepGE |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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