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randnet (version 1.0)

Random Network Model Estimation, Selection and Parameter Tuning

Description

Model fitting, model selection and parameter tuning procedures for a class of random network models. Many useful network modeling, estimation, and processing methods are included. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.

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Version

Install

install.packages('randnet')

Monthly Downloads

342

Version

1.0

License

GPL (>= 2)

Maintainer

Tianxi Li

Last Published

July 29th, 2025

Functions in randnet (1.0)

RightSC

clusters nodes in a directed network by regularized spectral clustering on right singular vectors
network.mixing.Bfold

estimates network connection probability by network mixing with B-fold averaging
nSmooth

estimates probabilty matrix by neighborhood smoothing
network.mixing

estimates network connection probability by network mixing
randnet-package

Statistical modeling of random networks with model estimation, selection and parameter tuning
reg.SP

clusters nodes by regularized spectral clustering
reg.SSP

detects communities by regularized spherical spectral clustering
ECV.Rank

estimates optimal low rank model for a network
NMI

calculates normalized mutual information
BlockModel.Gen

Generates networks from degree corrected stochastic block model
NSBM.Gen

Generates networks from nomination stochastic block model
ConsensusClust

clusters nodes by concensus (majority voting) initialized by regularized spectral clustering
ECV.nSmooth.lowrank

selecting tuning parameter for neighborhood smoothing estimation of graphon model
BHMC.estimate

Estimates the number of communities under block models by the spectral methods
NCV.select

selecting block models by NCV
NSBM.estimate

estimates nomination SBM parameters given community labels by the method of moments
USVT

estimates the network probability matrix by the improved universal singular value thresholding
ECV.block

selecting block models by ECV
DCSBM.estimate

Estimates DCSBM model
SBM.estimate

estimates SBM parameters given community labels
LRBIC

selecting number of communities by asymptotic likelihood ratio
LSM.PGD

estimates inner product latent space model by projected gradient descent
InformativeCore

identify the informative core component of a network
smooth.oracle

oracle smooth graphon estimation
RDPG.Gen

generates random networks from random dot product graph model
k.core

identify the K-core component of a network