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@udel.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
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
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
