Methods and Measures for Brain, Cognitive, and Psychometric
Network Analysis
![[R logo]](../../../doc/html/Rlogo.svg)
![[Up]](../../../doc/html/left.jpg)
![[Top]](../../../doc/html/up.jpg)
Documentation for package ‘NetworkToolbox’ version 1.4.2
Help Pages
| NetworkToolbox-package | NetworkToolbox-package |
| adapt.a | Adaptive Alpha |
| behavOpen | NEO-PI-3 for Resting-state Data |
| betweenness | Betweenness Centrality |
| binarize | Binarize Network |
| closeness | Closeness Centrality |
| clustcoeff | Clustering Coefficient |
| comcat | Communicating Nodes |
| comm.close | Community Closeness Centrality |
| comm.eigen | Community Eigenvector Centrality |
| comm.str | Community Strength/Degree Centrality |
| conn | Network Connectivity |
| convert2igraph | Convert Network(s) to igraph's Format |
| convertConnBrainMat | Import CONN Toolbox Brain Matrices to R format |
| cor2cov | Convert Correlation Matrix to Covariance Matrix |
| core.items | Core Items |
| cpm | Connectome-based Predictive Modeling |
| cpmEV | Connectome-based Predictive Modeling |
| cpmFP | Connectome-based Predictive Modeling |
| cpmFPperm | Connectome-based Predictive Modeling |
| cpmIV | Connectome-based Predictive Modeling |
| cpmIVperm | Connectome-based Predictive Modeling |
| cpmPlot | Connectome-based Predictive Modeling |
| dCor | Distance Correlation for ROI Time Series |
| dCor.parallel | Parallelization of Distance Correlation for ROI Time Series |
| degree | Degree |
| depend | Dependency Network Approach |
| depna | Dependency Neural Networks |
| desc | Variable Descriptive Statistics |
| desc.all | Dataset Descriptive Statistics |
| distance | Distance |
| diversity | Diversity Coefficient |
| ECO | ECO Neural Network Filter |
| ECOplusMaST | ECO+MaST Network Filter |
| edgerep | Edge Replication |
| eigenvector | Eigenvector Centrality |
| flow.frac | Flow Fraction |
| gain.functions | MFCF Gain Functions |
| gateway | Gateway Coefficient |
| gdcnv_lmfit | MFCF Gain Functions |
| gfcnv_logdet | MFCF Gain Functions |
| gfcnv_logdet_val | MFCF Gain Functions |
| hybrid | Hybrid Centrality |
| impact | Node Impact |
| is.graphical | Determines if Network is Graphical |
| kld | Kullback-Leibler Divergence |
| lattnet | Generates a Lattice Network |
| leverage | Leverage Centrality |
| LoGo | Local/Global Inversion Method |
| louvain | Louvain Community Detection Algorithm |
| MaST | Maximum Spanning Tree |
| MFCF | Maximally Filtered Clique Forest |
| neoOpen | NEO-PI-3 Openness to Experience Data |
| net.coverage | Network Coverage |
| network.coverage | Network Coverage |
| network.permutation | Permutation Test for Network Measures |
| NetworkToolbox | NetworkToolbox-package |
| neuralnetfilter | Neural Network Filter |
| openness | Four Inventories of Openness to Experience |
| openness.key | Four Inventories of Openness to Experience |
| participation | Participation Coefficient |
| pathlengths | Characteristic Path Lengths |
| plot.cpm | Plots CPM results |
| randnet | Generates a Random Network |
| reg | Regression Matrix |
| resp.rep | Repeated Responses Check |
| rmse | Root Mean Square Error |
| rspbc | Randomized Shortest Paths Betweenness Centrality |
| sim.chordal | Simulate Chordal Network |
| sim.swn | Simulate Small-world Network |
| smallworldness | Small-worldness Measure |
| stable | Stabilizing Nodes |
| strength | Node Strength |
| threshold | Threshold Network Estimation Methods |
| TMFG | Triangulated Maximally Filtered Graph |
| transitivity | Transitivity |
| un.direct | Convert Directed Network to Undirected Network |