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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "L0ggm" in publications use:'
type: software
license: AGPL-3.0-or-later
title: 'L0ggm: Smooth L0 Penalty Approximations for Gaussian Graphical Models'
version: 0.1.0
doi: 10.32614/CRAN.package.L0ggm
identifiers:
- type: doi
  value: 10.32614/CRAN.package.L0ggm
abstract: '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 <https://doi.org/10.1198/016214501753382273>;
  Zou & Li, 2008 <https://doi.org/10.1214/009053607000000802>) with five penalty functions:
  arctangent (Wang & Zhu, 2016 <https://doi.org/10.1155/2016/6495417>), EXP (Wang,
  Fan, & Zhu, 2018 <https://doi.org/10.1007/s10463-016-0588-3>), Gumbel, Log (Candes,
  Wakin, & Boyd, 2008 <https://doi.org/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.'
authors:
- family-names: Christensen
  given-names: Alexander
  email: alexpaulchristensen@gmail.com
  orcid: https://orcid.org/0000-0002-9798-7037
preferred-citation:
  type: manual
  title: 'L0ggm: Smooth L0 penalty approximations for Gaussian graphical models'
  authors:
  - family-names: Alexander
    given-names: Christensen,
  year: '2026'
  doi: 10.32614/CRAN.package.L0ggm
repository: https://alexchristensen.r-universe.dev
repository-code: https://github.com/AlexChristensen/L0ggm
commit: e44c8b134f7c83e6fc3dad11bf967a6b100e24b9
url: https://github.com/AlexChristensen/L0ggm
date-released: '2026-05-29'
contact:
- family-names: Christensen
  given-names: Alexander
  email: alexpaulchristensen@gmail.com
  orcid: https://orcid.org/0000-0002-9798-7037