Package: lglasso
Type: Package
Title: Graphical Lasso for Longitudinal Data
Version: 2.0.0
Description: Estimate treatment-specific precision matrices (networks) from longitudinal
    high-dimensional normal data. The corresponding random effects are also estimated. 
    It is motivated by the analysis of omics data in clinical trials where the longitudinal
    omics data becomes increasingly common. It includes both one-stage models (without 
    treatment) and two-stage models (with one treatment). For details of the algorithms, 
    please check the materials on its GitHub repo. If you have any questions, feel free to 
    contact the maintainers through the email below.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
URL: https://github.com/jiezhou-2/lglasso
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
Depends: R (>= 3.5)
Authors@R: c(
    person(given = "Jie", family = "Zhou",
           role = c("aut", "cre", "cph"),
           email = "chowstat@gmail.com"),
    person(given = "Jiang", family = "Gui",
           role = "aut"),
    person(given = "Weston", family = "Viles",
           role = "aut"),
    person(given = "Anne", family = "Hoen",
           role = "aut")
    )
NeedsCompilation: no
Packaged: 2026-09-24 02:07:36 UTC; f003r0s
Author: Jie Zhou [aut, cre, cph],
  Jiang Gui [aut],
  Weston Viles [aut],
  Anne Hoen [aut]
Maintainer: Jie Zhou <chowstat@gmail.com>
Repository: CRAN
Date/Publication: 2026-09-24 08:50:08 UTC
Imports: CVXR, glasso, MASS, fake, stats
RoxygenNote: 7.3.3
Config/testthat/edition: 3
