Title: Covariate Dependent Graph Estimation
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")))
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.
Imports: doParallel, foreach, ggplot2, glmnet, latex2exp, MASS,
Suggests: testthat (>= 3.0.0), covr, vdiffr

Packaged: 2022-09-16 15:25:55 UTC; jacob.a.helwig

Title: Produce Forest Plots to Visualize Covariate Effects
person("Samer", "Mouksassi", email = "samermouksassi@gmail.com",
Description: Produce forest plots to visualize covariate effects using either
the command line or an interactive 'Shiny' application.
Imports: colourpicker, dplyr, egg, ggplot2, ggstance, markdown, shiny,
Suggests: MASS, knitr, rmarkdown, ggridges
SystemRequirements: pandoc with https support

Suggests: MASS, knitr, rmarkdown, ggridges, plotly
Author: Samer Mouksassi [aut, cre] (<https://orcid.org/0000-0002-7152-6654>),

Imports: colourpicker, dplyr, egg, ggplot2, ggstance, ggridges,
markdown, shiny, shinyjs, stats, tidyr, utils
Suggests: MASS, knitr, rmarkdown, mrgsolve, ggrepel, table1, patchwork,

Imports: colourpicker, dplyr, egg, ggplot2 (>= 3.3.2), markdown, shiny,
Suggests: clipr, formatR, MASS, knitr, rmarkdown, mrgsolve, ggridges,
ggrepel, ggstance, patchwork, bayestestR, plotly, scales,