neoOpen

<p>A response matrix (<em>n</em> = 802) of NEO-PI-3's Openness to Experience
from Christensen, Cotter, &amp; Silvia (2019).</p>

datasets

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.

Alexander Christensen

NetworkToolbox

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

Guido Previde Massara

neoOpen function

Format

NEO-PI-3 Openness to Experience Data — neoOpen

NEO-PI-3 Openness to Experience Data

neoOpen: NEO-PI-3 Openness to Experience Data

Description

Usage

Arguments

Format

References

Examples