Random Network Model Estimation, Selection and Parameter Tuning

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Documentation for package ‘randnet’ version 0.7

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randnet-package Statistical modeling of random networks with model estimation, selection and parameter tuning
BHMC.estimate Estimates the number of communities under block models by the spectral methods
BlockModel.Gen Generates networks from degree corrected stochastic block model
ConsensusClust clusters nodes by concensus (majority voting) initialized by regularized spectral clustering
DCSBM.estimate Estimates DCSBM model
ECV.block selecting block models by ECV
ECV.nSmooth.lowrank selecting tuning parameter for neighborhood smoothing estimation of graphon model
ECV.Rank estimates optimal low rank model for a network
InformativeCore identify the informative core component of a network
LRBIC selecting number of communities by asymptotic likelihood ratio
LSM.PGD estimates inner product latent space model by projected gradient descent
NCV.select selecting block models by NCV
network.mixing estimates network connection probability by network mixing
network.mixing.Bfold estimates network connection probability by network mixing with B-fold averaging
NMI calculates normalized mutual information
NSBM.estimate estimates nomination SBM parameters given community labels by the method of moments
NSBM.Gen Generates networks from nomination stochastic block model
nSmooth estimates probabilty matrix by neighborhood smoothing
randnet Statistical modeling of random networks with model estimation, selection and parameter tuning
RDPG.Gen generates random networks from random dot product graph model
reg.SP clusters nodes by regularized spectral clustering
reg.SSP detects communities by regularized spherical spectral clustering
RightSC clusters nodes in a directed network by regularized spectral clustering on right singular vectors
SBM.estimate estimates SBM parameters given community labels
smooth.oracle oracle smooth graphon estimation
USVT estimates the network probability matrix by the improved universal singular value thresholding