The R Journal: article published in 2018, volume 10:2

NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis in R
Alexander P. Christensen , The R Journal (2018) 10:2, pages 422-439.

Abstract 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.

Received: 2018-06-07; online 2018-12-08, supplementary material, (2.2 KiB)
CRAN packages: NetworkToolbox, statnet, igraph, sna, brainGraph, qgraph, IsingFit, bootnet, glasso, psych, MVN, EGA, lavaan
CRAN Task Views implied by cited CRAN packages: Psychometrics, Optimization, SocialSciences, Bayesian, Econometrics, gR, Graphics, MissingData, OfficialStatistics, Spatial

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-065,
  author = {Alexander P. Christensen},
  title = {{NetworkToolbox: Methods and Measures for Brain, Cognitive,
          and Psychometric Network Analysis in R}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-065},
  url = {https://doi.org/10.32614/RJ-2018-065},
  pages = {422--439},
  volume = {10},
  number = {2}
}