covdepGE-package: covdepGE: Covariate Dependent Graph Estimation
In covdepGE: Covariate Dependent Graph Estimation
covdepGE-package R Documentation
covdepGE: Covariate Dependent Graph Estimation
Description
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.
Author(s)
Maintainer: Jacob Helwig jacob.a.helwig@tamu.edu
Authors:
Sutanoy Dasgupta sutanoy@stat.tamu.edu
Peng Zhao pzhao@stat.tamu.edu
Bani Mallick bmallick@stat.tamu.edu
Debdeep Pati debdeep@stat.tamu.edu
References
(1) Sutanoy Dasgupta, Peng Zhao, Prasenjit Ghosh, Debdeep Pati, and Bani
Mallick. An approximate Bayesian approach to covariate-dependent graphical
modeling. pages 1–59, 2022.
See Also
Useful links:
covdepGE documentation built on Sept. 16, 2022, 5:07 p.m.
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| covdepGE-package | R Documentation |
covdepGE: Covariate Dependent Graph Estimation
Description
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.
Author(s)
Maintainer: Jacob Helwig jacob.a.helwig@tamu.edu
Authors:
Sutanoy Dasgupta sutanoy@stat.tamu.edu
Peng Zhao pzhao@stat.tamu.edu
Bani Mallick bmallick@stat.tamu.edu
Debdeep Pati debdeep@stat.tamu.edu
References
(1) Sutanoy Dasgupta, Peng Zhao, Prasenjit Ghosh, Debdeep Pati, and Bani Mallick. An approximate Bayesian approach to covariate-dependent graphical modeling. pages 1–59, 2022.
See Also
Useful links:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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