Imports: Matrix, psych, corrplot, RColorBrewer, fdrtool, R.matlab,
MASS, hypergeo, pwr, graph, igraph, qgraph, RBGL, ppcor,

Imports: Matrix, psych, corrplot, fdrtool, R.matlab, MASS, pwr, igraph,
qgraph, IsingFit, ppcor, parallel, foreach, doSNOW

Description: 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-Gershogoren, 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.
qgraph, ppcor, parallel, foreach, doParallel

Authors@R: c(person("Alexander", "Christensen", email = "alexpaulchristensen@gmail.com", role = c("aut", "cre")), person("Guido", "Previde Massara", role = c("ctb")))
Imports: Matrix, psych, corrplot, fdrtool, R.matlab, MASS,
fitdistrplus, pwr, igraph, qgraph, ppcor, parallel, foreach,
Author: Alexander Christensen [aut, cre],

Authors@R: c(person("Alexander", "Christensen", email = "alexpaulchristensen@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9798-7037")), person("Guido", "Previde Massara", role = c("ctb"), comment = c(ORCID = "0000-0003-0502-2789")))
Author: Alexander Christensen [aut, cre]
Guido Previde Massara [ctb] (<https://orcid.org/0000-0003-0502-2789>)

Description: 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.
Imports: Matrix, psych, corrplot, fdrtool, R.matlab, igraph, qgraph,
ppcor, parallel, foreach, doParallel, MASS, pwr, IsingFit,