{ "_id": "6a103541acfb0bcc41c999e6", "Package": "NetworkToolbox", "Title": "Methods and Measures for Brain, Cognitive, and Psychometric\nNetwork Analysis", "Version": "1.4.4", "Date": "2025-05-08", "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\")))", "Maintainer": "Alexander Christensen ", "Description": "Implements network analysis and graph theory measures used\nin neuroscience, cognitive science, and psychology. Methods\ninclude various filtering methods and approaches such as\nthreshold, dependency (Kenett, Tumminello, Madi,\nGur-Gershgoren, Mantegna, & Ben-Jacob, 2010\n), Information Filtering\nNetworks (Barfuss, Massara, Di Matteo, & Aste, 2016\n), and Efficiency-Cost\nOptimization (Fallani, Latora, & Chavez, 2017\n). Brain methods include the\nrecently developed Connectome Predictive Modeling (see\nreferences in package). Also implements several network\nmeasures including local network characteristics (e.g.,\ncentrality), community-level network characteristics (e.g.,\ncommunity centrality), global network characteristics (e.g.,\nclustering coefficient), and various other measures associated\nwith the reliability and reproducibility of network analysis.", "License": "GPL (>= 3.0)", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libjpeg-dev\nlibpng-dev libuv1-dev libxml2-dev", "Repository": "https://alexchristensen.r-universe.dev", "Date/Publication": "2025-05-08 15:54:22 UTC", "RemoteUrl": "https://github.com/alexchristensen/networktoolbox", "RemoteRef": "HEAD", "RemoteSha": "d41da6d7bbf6165d358ebf55470531c6c876c18c", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-12 07:27:05 UTC", "User": "root" }, "Author": "Alexander Christensen [aut, cre] (ORCID:\n),\nGuido Previde Massara [ctb] (ORCID:\n)", "MD5sum": 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