Package: NetworkToolbox 1.5.0

NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis

Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Several network measures are translated from the Brain Connectivity Toolbox (Rubinov & Sporns, 2010 <doi:10.1016/j.neuroimage.2009.10.003>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.

Authors:Alexander Christensen [aut, cre], Guido Previde Massara [ctb], BCT Contributors [cph], Mika Rubinov [cph, ctb], Olaf Sporns [cph, ctb], Alex Fornito [cph, ctb], Jeff Spielberg [cph, ctb], Jonathan Clayden [cph, ctb], Rick Betzel [cph, ctb], Andrea Avena [cph, ctb], Alexandros Goulas [cph, ctb]