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NetworkToolbox (version 1.1.1)

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

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 ), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 ), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 ). 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), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.

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Version

Install

install.packages('NetworkToolbox')

Monthly Downloads

2,053

Version

1.1.1

License

GPL (>= 3.0)

Maintainer

Alexander Christensen

Last Published

March 25th, 2018

Functions in NetworkToolbox (1.1.1)

Triangulated Maximally Filtered Graph

Animal Verbal Fluency Data

Planar Maximally Filtered Graph

Maximum Spanning Tree

ECO+MaST Network Filter

NEO-PI-3 for Resting-state Data

convertConnBrainMat

Import CONN Toolbox Brain Matrices to R format

Convert Network(s) to igraph's Format

ECO Neural Network Filter

Clustering Coefficient

Bootstrapped Network Generalization Plots

Convert Correlation Matrix to Covariance Matrix

Connectome-based Predictive Modeling--Fingerprinting

List of Centrality Measures

Connectome-based Predictive Modeling--Fingerprinting Permutation

Bootstrapped Communities Likelihood

Bootstrapped Network Generalization

Closeness Centrality

Dependency Neural Networks

Network Adjusted Mean/Sum

Neural-Behavioral Correlation Test

Network Connectivity

Generates a Lattice Network

Edge Replication

Connectome-based Predictive Modeling--Internal Validation

Determines if Network is Graphical

Eigenvector Centrality

NEO-PI-3 Openness to Experience Data

Hybrid Centrality

Leverage Centrality

Generates a Random Network

Dependency Network Approach

neuralnetfilter

Neural Network Filter

Local and Global Neural Network Characteristics

neuralgrouptest

Neural Network Group Statistics Tests

Connectome-based Predictive Modeling--External Validation

Small-worldness Measure

Partial Bootstrapped Semantic Network Analysis

Louvain Community Detection Algorithm

Semantic Network Measures

Regression Matrix

Network Construction for splitsamp

Threshold Filter

Randomized Shortest Paths Betweenness Centrality

Characteristic Path Lengths

Statistics for splitsamp Networks

Binarize Network

Betwenness Centrality

Local/Global Sparse Inverse Covariance Matrix