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NetworkToolbox (version 1.1.2)

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

1.1.2

License

GPL (>= 3.0)

Maintainer

Alexander Christensen

Last Published

April 29th, 2018

Functions in NetworkToolbox (1.1.2)

LoGo

Local/Global Sparse Inverse Covariance Matrix
bootgen

Bootstrapped Network Generalization
bootgenPlot

Bootstrapped Network Generalization Plots
cpmIV

Connectome-based Predictive Modeling--Internal Validation
degree

Degree
louvain

Louvain Community Detection Algorithm
leverage

Leverage Centrality
neuralcorrtest

Neural-Behavioral Correlation Test
neuralgrouptest

Neural Network Group Statistics Tests
rmse

Root Mean Square Error
rspbc

Randomized Shortest Paths Betweenness Centrality
strength

Node Strength
threshold

Threshold Filter
ECO

ECO Neural Network Filter
ECOplusMaST

ECO+MaST Network Filter
NetworkToolbox-package

NetworkToolbox--package
clustcoeff

Clustering Coefficient
comcat

Communicating Nodes
TMFG

Triangulated Maximally Filtered Graph
convert2igraph

Convert Network(s) to igraph's Format
diversity

Diversity Coefficient
distance

Distance
edgerep

Edge Replication
behavOpen

NEO-PI-3 for Resting-state Data
animals

Animal Verbal Fluency Data
eigenvector

Eigenvector Centrality
neuralnetfilter

Neural Network Filter
commboot

Bootstrapped Communities Likelihood
convertConnBrainMat

Import CONN Toolbox Brain Matrices to R format
neuralstat

Local and Global Neural Network Characteristics
depend

Dependency Network Approach
splitsampNet

Network Construction for splitsamp
splitsamp

Split sample
conn

Network Connectivity
cpmFPperm

Connectome-based Predictive Modeling--Fingerprinting Permutation
cpmFP

Connectome-based Predictive Modeling--Fingerprinting
gateway

Gateway Coefficient
depna

Dependency Neural Networks
kld

Kullback-Leibler Divergence
hybrid

Hybrid Centrality
randnet

Generates a Random Network
reg

Regression Matrix
sim.swn

Simulate Small-world Network
lattnet

Generates a Lattice Network
smallworldness

Small-worldness Measure
nams

Network Adjusted Mean/Sum
semnetboot

Partial Bootstrapped Semantic Network Analysis
neoOpen

NEO-PI-3 Openness to Experience Data
semnetmeas

Semantic Network Measures
betweenness

Betwenness Centrality
transitivity

Transitivity
binarize

Binarize Network
centlist

List of Centrality Measures
closeness

Closeness Centrality
cor2cov

Convert Correlation Matrix to Covariance Matrix
cpmEV

Connectome-based Predictive Modeling--External Validation
impact

Node Impact
is.graphical

Determines if Network is Graphical
participation

Participation Coefficient
pathlengths

Characteristic Path Lengths
splitsampStats

Statistics for splitsamp Networks
stable

Stabilizing Nodes
MaST

Maximum Spanning Tree