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.
Package details |
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Maintainer | |
License | GPL (>= 3) |
Version | 1.0.1 |
URL | https://github.com/JacobHelwig/covdepGE |
Package repository | View on GitHub |
Installation |
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