Network-Based Communities and Kernel Machine Methods
Simulated group networks
Hamiltonian distance matrix creation
Multimodal heatbath algorithm
Hierarchical multimodal spinglass algorithm
Distance-based kernel
Functional and Structural Matrix Plot
Convert matrices to dataframe list for network
Adjusted Rand Index (ARI)
CommKern
Community Allegiance
Communities by layer plot
Compute modularity matrix
Compute multimodal modularity matrix
Consensus Similarity
Count pairs
Node degree calculation
Entropy
Extrinsic evaluation distance matrix creation
Starting temperature
Simulated network edge weights
Group adjacency matrices
Normalized mutual information (NMI)
Purity
Nonparametric score function for distance-based kernel and continuous ...
Semiparametric score function for distance-based kernel and continuous...
Nonparametric score function for distance-based kernel and binary outc...
Semiparametric score function for distance-based kernel
Simulated network data frame
Sort pairs
Convert matrices to list of data frames for subnetworks
Trace
Bounds of grid search function
Rand z-score
Analysis of network community objects with applications to neuroimaging data. There are two main components to this package. The first is the hierarchical multimodal spinglass (HMS) algorithm, which is a novel community detection algorithm specifically tailored to the unique issues within brain connectivity. The other is a suite of semiparametric kernel machine methods that allow for statistical inference to be performed to test for potential associations between these community structures and an outcome of interest (binary or continuous).