Latent Variable Network Modeling

Estimate, fit and compare Structural Equation Models (SEM) and network models (Gaussian Graphical Models; GGM) using OpenMx. Allows for two possible generalizations to include GGMs in SEM: GGMs can be used between latent variables (latent network modeling; LNM) or between residuals (residual network modeling; RNM). For details, see Epskamp, Rhemtulla and Borsboom (2017) .


Changes in Version 0.3: o 'scale' now defaults to FALSE! o Due to this change, some values such as the BIC will be different o Fixed a bug where higher order latent variables were not usuable o Added the 'mimic' argument. mimic = "lavaan" will mimic lavaan results (now default). o lvnet should now return the same fit as lavaan when is used in lavaan. o Added the function 'lav2lvnet' to easily generate lvnet input from a lavaan model o Added 'ggmFit' to fit a given partial correlation network (Gaussian graphical model; GGM)

Reference manual

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0.3.2 by Sacha Epskamp, 3 months ago

Browse source code at

Authors: Sacha Epskamp

Documentation:   PDF Manual  

GPL-2 license

Imports glasso, qgraph, Matrix, psych, mvtnorm, parallel, corpcor, dplyr, methods, lavaan, semPlot

Depends on OpenMx

See at CRAN