Package: covdepGE
Title: Covariate Dependent Graph Estimation
Version: 1.0.1
Authors@R: 
    c(person("Jacob", "Helwig", email = "jacob.a.helwig@tamu.edu", role = c("cre", "aut")),
    person("Sutanoy", "Dasgupta", email = "sutanoy@stat.tamu.edu", role = c("aut")),
    person("Peng", "Zhao", email = "pzhao@stat.tamu.edu", role = c("aut")),
    person("Bani", "Mallick", email = "bmallick@stat.tamu.edu", role = c("aut")),
    person("Debdeep", "Pati", email = "debdeep@stat.tamu.edu", role = c("aut")))
Date: 2022-09-16
Language: en-US
BugReports: https://github.com/JacobHelwig/covdepGE/issues
URL: https://github.com/JacobHelwig/covdepGE
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. 
License: GPL (>= 3)
Encoding: UTF-8
RoxygenNote: 7.2.1
LinkingTo: Rcpp, RcppArmadillo
Imports: doParallel, foreach, ggplot2, glmnet, latex2exp, MASS,
        parallel, Rcpp, reshape2, stats
Suggests: testthat (>= 3.0.0), covr, vdiffr
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2022-09-16 15:25:55 UTC; jacob.a.helwig
Author: Jacob Helwig [cre, aut],
  Sutanoy Dasgupta [aut],
  Peng Zhao [aut],
  Bani Mallick [aut],
  Debdeep Pati [aut]
Maintainer: Jacob Helwig <jacob.a.helwig@tamu.edu>
Repository: CRAN
Date/Publication: 2022-09-16 15:56:08 UTC
Built: R 4.2.0; aarch64-apple-darwin20; 2023-07-11 00:48:27 UTC; unix
Archs: covdepGE.so.dSYM
