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). 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.
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