Smooth L0 Penalty Approximations for Gaussian Graphical Models R package L0ggm version 0.0.1 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. Software Depends: R (>= 3.5.0) Imports: igraph, glasso, glassoFast, Matrix, methods, psych, stats Alexander Christensen <alexpaulchristensen@gmail.com> Comprehensive R Archive Network (CRAN) 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), Alexander Robitzsch [cph, ctb] (C++ implementation of Drezner-Wesolowsky bivariate normal CDF in polychoric_matrix.c), David Blackman [ctb] (Original xoshiro.c implementation), Sebastiano Vigna [ctb] (Original xoshiro.c implementation), John Burkardt [cph, ctb] (Original ziggurat.c implementation) AGPL (>= 3.0) 2026-03-26 application/tgz https://CRAN.R-project.org/package=L0ggm doi:10.32614/CRAN.package.L0ggm