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networksem (version 0.4)

Network Structural Equation Modeling

Description

Several methods have been developed to integrate structural equation modeling techniques with network data analysis to examine the relationship between network and non-network data. Both node-based and edge-based information can be extracted from the network data to be used as observed variables in structural equation modeling. To facilitate the application of these methods, model specification can be performed in the familiar syntax of the 'lavaan' package, ensuring ease of use for researchers. Technical details and examples can be found at .

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Version

Install

install.packages('networksem')

Monthly Downloads

149

Version

0.4

License

GPL

Maintainer

Zhiyong Zhang

Last Published

May 9th, 2025

Functions in networksem (0.4)

sem.net.addvar.stat

Compute a list of user-specified network statistics values using the "sna" package and add them to the non-network data.
sem.net.lsm

Fit a Structural Equation Model (SEM) with both network and non-network data by incorporating network latent positions as variables.
sem.net.addvar

Compute user-specified network statistics for a specific network.
sem.net.addvar.influential

Compute a list of user-specified network statistics using the "influential" package and add it to the existing data.
path.networksem

Calculate a mediation effect from a networksem model
friend_data

Friendship network data
sem.net.edge

Fit a Structural Equation Model (SEM) with both network and non-network data by transforming nonnetwork data into paired values corresponding to network edge values.
sem.net.edge.lsm

Fit a Structural Equation Model (SEM) with both network and non-network data by transforming nonnetwork data into paired values corresponding to network latent distance pairs.
summary.networksem

Summarize output from networksem functions includeing sem.net, sem.net.lsm, sem.net.edge, sem.net.edge.lsm.
sem.net

Fit a Structural Equation Model (SEM) with both network and non-network data by incorporating node-level network statistics as variables.