Package: grasps
Type: Package
Title: Groupwise Regularized Adaptive Sparse Precision Solution
Version: 0.1.1
Authors@R: c(
    person(given = "Shiying", family = "Xiao",
           email = "shiying.xiao@outlook.com",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-8846-3258")),
    person(given = "Jun", family = "Yan",
           email = "jun.yan@uconn.edu",
           role = "aut",
           comment = c(ORCID = "0000-0003-4401-7296")),
    person(given = "Panpan", family = "Zhang",
           email = "panpan.zhang@vumc.org",
           role = "aut",
           comment = c(ORCID = "0000-0002-8211-5930"))
  )
Maintainer: Shiying Xiao <shiying.xiao@outlook.com>
Description: Provides a unified framework for sparse-group
        regularization and precision matrix estimation in Gaussian
        graphical models. It implements multiple sparse-group
        penalties, including sparse-group lasso, sparse-group adaptive
        lasso, sparse-group SCAD, and sparse-group MCP, and solves them
        efficiently using ADMM-based optimization. The package is
        designed for high-dimensional network inference where both
        sparsity and group structure are present.
License: GPL (>= 3)
URL: https://github.com/Carol-seven/grasps,
        https://shiying-xiao.com/grasps/
BugReports: https://github.com/Carol-seven/grasps/issues
Encoding: UTF-8
Imports: igraph, ggforce, ggplot2, grDevices, Rcpp, Rdpack, scales
LinkingTo: Rcpp, RcppArmadillo
RdMacros: Rdpack
Suggests: knitr, MASS, quarto, rmarkdown
VignetteBuilder: knitr, quarto
Config/roxygen2/version: 8.0.0
Config/pak/sysreqs: libfontconfig1-dev libfreetype6-dev libglpk-dev
        libxml2-dev
Repository: https://carol-seven.r-universe.dev
Date/Publication: 2026-05-02 23:56:16 UTC
RemoteUrl: https://github.com/carol-seven/grasps
RemoteRef: HEAD
RemoteSha: c781394389d3f677d4fd5f1d236cafb72732d79e
NeedsCompilation: yes
Packaged: 2026-07-02 09:23:40 UTC; root
Author: Shiying Xiao [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-8846-3258>),
  Jun Yan [aut] (ORCID: <https://orcid.org/0000-0003-4401-7296>),
  Panpan Zhang [aut] (ORCID: <https://orcid.org/0000-0002-8211-5930>)