Package: L0ggm
Title: Smooth L0 Penalty Approximations for Gaussian Graphical Models
Version: 0.1.2
Date: 2026-07-20
Authors@R: c(
    person("Alexander", "Christensen", email = "alexpaulchristensen@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9798-7037")),
    person("Jeongwon", "Choi", email = "jungwon799@gmail.com", role = "ctb", comment = c(ORCID = "0000-0001-6087-2124")),
    person("John", "Fox", role = c("cph", "ctb"), comment = "Original implementation of polyserial correlations in auto_correlate.R"),
    person("Yves", "Rosseel", role = c("cph", "ctb"), comment = "Original implementation of rmsea_ci in network_fit.R"),
    person("Alan", "Genz", role = c("cph", "ctb"), comment = "Fortran implementation of the bivariate normal CDF (TVPACK MVBVU) in polychoric_matrix.c, translated via the pbivnorm R package"),
    person("David", "Blackman", role = "ctb", comment = "Original xoshiro.c implementation"),
    person("Sebastiano", "Vigna", role = "ctb", comment = "Original xoshiro.c implementation"),
    person("John", "Burkardt", role = c("cph", "ctb"), comment = "Original ziggurat.c implementation")
   )
Description: Provides smooth approximations to the L0 norm penalty for
        estimating sparse Gaussian graphical models (GGMs). Network
        estimation is performed using the Local Linear Approximation
        (LLA) framework (Fan & Li, 2001
        <doi:10.1198/016214501753382273>; Zou & Li, 2008
        <doi:10.1214/009053607000000802>) with five penalty functions:
        arctangent (Wang & Zhu, 2016 <doi:10.1155/2016/6495417>), EXP
        (Wang, Fan, & Zhu, 2018 <doi:10.1007/s10463-016-0588-3>),
        Gumbel, Log (Candes, Wakin, & Boyd, 2008
        <doi:10.1007/s00041-008-9045-x>), and Weibull. Adaptive penalty
        parameters for EXP, Gumbel, and Weibull are estimated via
        maximum likelihood, and model selection uses information
        criteria including AIC, BIC, and EBIC (Extended BIC).
        Simulation functions generate multivariate normal data from
        GGMs with stochastic block model or small-world
        (Watts-Strogatz) network structures.
Depends: R (>= 3.5.0)
License: AGPL (>= 3.0)
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
Imports: igraph, glasso, glassoFast, Matrix, methods, psych, stats
Copyright: See inst/COPYRIGHTS for details
BugReports: https://github.com/AlexChristensen/L0ggm/issues
Config/roxygen2/version: 8.0.0
Config/pak/sysreqs: libglpk-dev libxml2-dev
Repository: https://alexchristensen.r-universe.dev
Date/Publication: 2026-07-20 15:17:44 UTC
RemoteUrl: https://github.com/alexchristensen/l0ggm
RemoteRef: HEAD
RemoteSha: 8f9938f7805aefc229221830b57266a956078eae
Packaged: 2026-07-20 15:56:04 UTC; root
Author: Alexander Christensen [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-9798-7037>),
  Jeongwon Choi [ctb] (ORCID: <https://orcid.org/0000-0001-6087-2124>),
  John Fox [cph, ctb] (Original implementation of polyserial correlations
    in auto_correlate.R),
  Yves Rosseel [cph, ctb] (Original implementation of rmsea_ci in
    network_fit.R),
  Alan Genz [cph, ctb] (Fortran implementation of the bivariate normal
    CDF (TVPACK MVBVU) in polychoric_matrix.c, translated via the
    pbivnorm R package),
  David Blackman [ctb] (Original xoshiro.c implementation),
  Sebastiano Vigna [ctb] (Original xoshiro.c implementation),
  John Burkardt [cph, ctb] (Original ziggurat.c implementation)
Maintainer: Alexander Christensen <alexpaulchristensen@gmail.com>