Random Network Model Selection and Parameter Tuning

Documentation for package ‘randnet’ version 0.2

randnet-package	Statistical modeling of random networks with model 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
LRBIC	selecting number of communities by asymptotic likelihood ratio
NCV.select	selecting block models by NCV
NMI	calculates normalized mutual information
nSmooth	estimates probabilty matrix by neighborhood smoothing
randnet	Statistical modeling of random networks with model 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
SBM.estimate	estimates SBM parameters given community labels