GGMncv-package: GGMncv: Gaussian Graphical Models with Nonconvex...
In GGMncv: Gaussian Graphical Models with Nonconvex Regularization

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The primary goal of GGMncv is to provide non-convex penalties for estimating Gaussian graphical models. These are known to overcome the various limitations of lasso (least absolute shrinkage "screening" operator), including inconsistent model selection \insertCitezhao2006modelGGMncv, biased estimates \insertCitezhang2010nearlyGGMncv, and a high false positive rate \insertCite@see for example @williams2020back;@williams2019nonregularizedGGMncv

Several of the penalties are (continuous) approximations to the \mjseqn\ell_0 penalty, that is, best subset selection. However, the solution does not require enumerating all possible models which results in a computationally efficient solution.

L0 Approximations

Atan: penalty = "atan" \insertCitewang2016variableGGMncv. This is currently the default.
Seamless \mjseqn\ell_0: penalty = "selo" \insertCitedicker2013variableGGMncv.
Exponential: penalty = "exp" \insertCitewang2018variableGGMncv
Log: penalty = "log" \insertCitemazumder2011sparsenetGGMncv.
Sica: penalty = "sica" \insertCitelv2009unifiedGGMncv

Additional penalties:

SCAD: penalty = "scad" \insertCitefan2001variableGGMncv.
MCP: penalty = "mcp" \insertCitezhang2010nearlyGGMncv.
Adaptive lasso: penalty = "adapt" \insertCitezou2006adaptiveGGMncv.
Lasso: penalty = "lasso" \insertCitetibshirani1996regressionGGMncv.

Citing GGMncv

It is important to note that GGMncv merely provides a software implementation of other researchers work. There are no methodological innovations, although this is the most comprehensive R package for estimating GGMs with non-convex penalties. Hence, in addition to citing the package citation("GGMncv"), it is important to give credit to the primary sources. The references are provided above and in ggmncv.

Further, a survey (or review) of these penalties can be found in \insertCitewilliams2020beyond;textualGGMncv.

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GGMncv documentation built on Dec. 15, 2021, 9:10 a.m.