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>
License: GPL (≥ 3)
NeedsCompilation: yes
Language: en-US
Materials: README
CRAN checks: covdepGE results


Reference manual: covdepGE.pdf


Package source: covdepGE_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): covdepGE_1.0.1.tgz, r-oldrel (arm64): covdepGE_1.0.1.tgz, r-release (x86_64): covdepGE_1.0.1.tgz, r-oldrel (x86_64): covdepGE_1.0.1.tgz
Old sources: covdepGE archive


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