Smooth L0 Penalty Approximations for Gaussian Graphical Models
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK7 OK2026-03-27
Documentation
- Help pages
- 15includes a package overview page
- Examples that run
- 100%14 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 67%
Downloads
Repository
Commits over time
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Checks run against github.com/alexchristensen/l0ggm on 2026-10-25.
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Dependencies
Installing L0ggm adds 14 packages (9 with compiled code, 11 MB of source tarballs). Heaviest: igraph 4.8 MB, GPArotation 1.6 MB, psych 1.1 MB.
Code & Tests
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- 0.1.2Latest
- 0.1.12026-07-06 · diff ↗
- 0.1.0
- RR 4.6.0 released · 2026-04-24
- 0.0.12026-03-26
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-03-26
- Total releases
- 4 / 1 yrs
- License
- AGPL (>= 3.0)
- Copyright
- See inst/COPYRIGHTS for details
- Minimum R
- ≥ 3.5.0
- Bundled data
- 83 KB / 3 files
- Download size
- 2.3 MB
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citation("L0ggm")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.32614/cran.package.l0ggm.
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