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) .


News

Changes in Version 0.3.2 o Summary output now contains parameter labels o Equality constraints now supported by supplying labels in model matrices

Changes in Version 0.3.1: o 'ggmFit' is now moved to qgraph and exported by lvnet o Several updates to ggmFit in qgraph o Added 'lvnetRefit' to refit an lvnet model to new data

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 std.lv=TRUE 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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install.packages("lvnet")

0.3.3 by Sacha Epskamp, 2 months ago


Browse source code at https://github.com/cran/lvnet


Authors: Sacha Epskamp


Documentation:   PDF Manual  


Task views: gRaphical Models in R


GPL-2 license


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

Depends on OpenMx


See at CRAN