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L0ggm  

Smooth L0 Penalty Approximations for Gaussian Graphical Models
View on CRAN: Click here


Download and install L0ggm package within the R console
Install from CRAN:
install.packages("L0ggm")

Install from Github:
library("remotes")
install_github("cran/L0ggm")

Install by package version:
library("remotes")
install_version("L0ggm", "0.0.1")



Attach the package and use:
library("L0ggm")
Maintained by
Alexander Christensen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-26
Latest Update: 2026-03-26
Description:
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.
How to cite:
Alexander Christensen (2026). L0ggm: Smooth L0 Penalty Approximations for Gaussian Graphical Models. R package version 0.0.1, https://cran.r-project.org/web/packages/L0ggm. Accessed 05 Jun. 2026.
Previous versions and publish date:
0.0.1 (2026-03-26 10:30)
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Complete documentation for L0ggm
Functions, R codes and Examples using the L0ggm R package
Full L0ggm package functions and examples
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