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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 |
| Reference manual: | covdepGE.pdf |
| Package source: | covdepGE_1.0.1.tar.gz |
| Windows binaries: | r-devel: covdepGE_1.0.1.zip, r-release: covdepGE_1.0.1.zip, r-oldrel: covdepGE_1.0.1.zip |
| 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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