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.
| Version: |
1.0.1 |
| Imports: |
doParallel, foreach, ggplot2, glmnet, latex2exp, MASS, parallel, Rcpp, reshape2, stats |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
testthat (≥ 3.0.0), covr, vdiffr |
| Published: |
2022-09-16 |
| DOI: |
10.32614/CRAN.package.covdepGE |
| Author: |
Jacob Helwig [cre, aut],
Sutanoy Dasgupta [aut],
Peng Zhao [aut],
Bani Mallick [aut],
Debdeep Pati [aut] |
| Maintainer: |
Jacob Helwig <jacob.a.helwig at tamu.edu> |
| BugReports: |
https://github.com/JacobHelwig/covdepGE/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/JacobHelwig/covdepGE |
| NeedsCompilation: |
yes |
| Language: |
en-US |
| Materials: |
README |
| CRAN checks: |
covdepGE results |
Documentation:
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