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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=covdepGE to link to this page.
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