L0ggm: Smooth L0 Penalty Approximations for Gaussian Graphical Models
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.
| Version: | 0.0.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | igraph, glasso, glassoFast, Matrix, methods, psych, stats |
| Published: | 2026-03-26 |
| DOI: | 10.32614/CRAN.package.L0ggm |
| Author: | Alexander Christensen
 [aut, cre],
Jeongwon Choi
[ctb],
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) |
| Maintainer: | Alexander Christensen <alexpaulchristensen at gmail.com> |
| BugReports: | https://github.com/AlexChristensen/L0ggm/issues |
| License: | AGPL (≥ 3.0) |
| Copyright: | See inst/COPYRIGHTS for details L0ggm copyright details |
| NeedsCompilation: | yes |
| Citation: | L0ggm citation info |
| Materials: | NEWS |
| CRAN checks: | L0ggm results |
Documentation:
| Reference manual: | L0ggm.html , L0ggm.pdf |
Downloads:
| Package source: | L0ggm_0.0.1.tar.gz |
| Windows binaries: | r-devel: L0ggm_0.0.1.zip, r-release: L0ggm_0.0.1.zip, r-oldrel: L0ggm_0.0.1.zip |
| macOS binaries: | r-release (arm64): L0ggm_0.0.1.tgz, r-oldrel (arm64): not available, r-release (x86_64): L0ggm_0.0.1.tgz, r-oldrel (x86_64): L0ggm_0.0.1.tgz |
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