This article introduces the NetworkToolbox package for R. Network analysis offers an intuitive perspective on complex phenomena via models depicted by nodes (variables) and edges (correlations). The ability of networks to model complexity has made them the standard approach for modeling the intricate interactions in the brain. Similarly, networks have become an increasingly attractive model for studying the complexity of psychological and psychopathological phenomena. NetworkToolbox aims to provide researchers with state-of-the-art methods and measures for es timating and analyzing brain, cognitive, and psychometric networks. In this article, I introduce NetworkToolbox and provide a tutorial for applying some the package’s functions to personality data.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-065.zip
NetworkToolbox, statnet, igraph, sna, brainGraph, qgraph, IsingFit, bootnet, glasso, psych, MVN, EGA, lavaan
Psychometrics, Optimization, SocialSciences, Bayesian, Econometrics, gR, Graphics, MissingData, OfficialStatistics, Spatial
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Christensen, "The R Journal: NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis in R", The R Journal, 2018
BibTeX citation
@article{RJ-2018-065,
author = {Christensen, Alexander P.},
title = {The R Journal: NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis in R},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-065},
doi = {10.32614/RJ-2018-065},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {422-439}
}