Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis
Closeness Centrality
Clustering Coefficient
Adaptive Alpha
Betweenness Centrality
Binarize Network
Communicating Nodes
Community Closeness Centrality
Community Eigenvector Centrality
Community Strength/Degree Centrality
Network Connectivity
Convert Network(s) to igraph's Format
Import CONN Toolbox Brain Matrices to R format
Convert Correlation Matrix to Covariance Matrix
Core Items
Connectome-based Predictive Modeling
Parallelization of Distance Correlation for ROI Time Series
Distance Correlation for ROI Time Series
Degree
Dependency Network Approach
Dependency Neural Networks
Dataset Descriptive Statistics
Variable Descriptive Statistics
Distance
Diversity Coefficient
ECO Neural Network Filter
ECO+MaST Network Filter
Edge Replication
Eigenvector Centrality
Flow Fraction
MFCF Gain Functions
Gateway Coefficient
Hybrid Centrality
Node Impact
Determines if Network is Graphical
Kullback-Leibler Divergence
Generates a Lattice Network
Leverage Centrality
Local/Global Inversion Method
Louvain Community Detection Algorithm
Maximum Spanning Tree
Maximally Filtered Clique Forest
Network Coverage
Network Coverage
Permutation Test for Network Measures
NetworkToolbox--package
Neural Network Filter
Participation Coefficient
Characteristic Path Lengths
Plots CPM results
Generates a Random Network
Regression Matrix
Repeated Responses Check
Root Mean Square Error
Randomized Shortest Paths Betweenness Centrality
Simulate Chordal Network
Simulate Small-world Network
Small-worldness Measure
Stabilizing Nodes
Node Strength
Threshold Network Estimation Methods
Triangulated Maximally Filtered Graph
Transitivity
Convert Directed Network to Undirected Network
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.