L0ggm
0.1.1Smooth 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
- OK2026-03-277 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 67%
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Repository
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Checks run against github.com/alexchristensen/l0ggm on 2026-07-19.
Dependencies
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Code & Tests
- Cyclomatic complexity
- 1.0 median / 13 max
Test coverage
Line coverage
–
Expression
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Tests / Examples
–
Functions
177 11 exported
Complexity
2.5 avg / 13 max
Call network
177 nodes / 266 edges
Call graph
Open call graph →Lowest coverage
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People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- 0.1.1Latest
- 0.1.02026-05-16 · diff ↗
- 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
- 3 / 1 yrs
- License
- AGPL (>= 3.0)
- Minimum R
- ≥ 3.5.0
- Bundled data
- 83 KB / 3 files
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet