  In recent literature, the Gaussian Graphical model (GGM; Lauritzen, 1996),a
network of partial correlation coefficients, has been used to capture potential
dynamic relationships between observed variables. The GGM can be estimated
using regularization in combination with model selection using the extended
Bayesian Information Criterion (Foygel and Drton, 2010). I term this
methodology GeLasso, and asses its performance using a plausible psychological
network structure with both continuous and ordinal datasets.Simulation results
indicate that GeLasso works well as an out-of-the-box method to estimate
network structures.
