Package: randnet 1.0

randnet: Random Network Model Estimation, Selection and Parameter Tuning

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.

Authors:Tianxi Li [aut, cre], Elizeveta Levina [aut], Ji Zhu [aut], Can M. Le [aut]

randnet_1.0.tar.gz
randnet_1.0.zip(r-4.7)randnet_1.0.zip(r-4.6)randnet_1.0.zip(r-4.5)
randnet_1.0.tgz(r-4.6-x86_64)randnet_1.0.tgz(r-4.6-arm64)randnet_1.0.tgz(r-4.5-x86_64)randnet_1.0.tgz(r-4.5-arm64)
randnet_1.0.tar.gz(r-4.7-arm64)randnet_1.0.tar.gz(r-4.7-x86_64)randnet_1.0.tar.gz(r-4.6-arm64)randnet_1.0.tar.gz(r-4.6-x86_64)
randnet_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
randnet/json (API)

# Install 'randnet' in R:
install.packages('randnet', repos = c('https://tianxili.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

2.90 score 3 packages 88 scripts 258 downloads 1 mentions 25 exports 66 dependencies

Last updated from:4f6eba6afd. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK191
linux-devel-x86_64OK182
source / vignettesOK186
linux-release-arm64OK201
linux-release-x86_64OK191
macos-release-arm64OK135
macos-release-x86_64OK409
macos-oldrel-arm64OK161
macos-oldrel-x86_64OK391
windows-develOK160
windows-releaseOK166
windows-oldrelOK148
wasm-releaseOK114

Exports:BHMC.estimateBlockModel.GenConsensusClustDCSBM.estimateECV.blockECV.nSmooth.lowrankECV.RankInformativeCorek.coreLRBICLSM.PGDNCV.selectnetwork.mixingnetwork.mixing.BfoldNMINSBM.estimateNSBM.GennSmoothRDPG.Genreg.SPreg.SSPRightSCSBM.estimatesmooth.oracleUSVT

Dependencies:abindashAUCbitopsbootcliclustercolorspacecpp11data.tabledeSolveentropyfarverfdafdsFNNgamm4ggplot2gluegrpreggtablehdrcdeirlbaisobandkernlabKernSmoothkslabelinglatticelifecyclelme4locfitmagicMASSMatrixmclustmgcvminqamulticoolmvtnormnlmenloptrnnlspbspcaPPpoweRlawpracmaR6rainbowrbibutilsRColorBrewerRcppRcppEigenRCurlRdpackreformulasrefundrlangRLRsimRSpectraS7scalessparseFLMMvctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Statistical modeling of random networks with model estimation, selection and parameter tuningrandnet-package randnet
Estimates the number of communities under block models by the spectral methodsBHMC.estimate
Generates networks from degree corrected stochastic block modelBlockModel.Gen
clusters nodes by concensus (majority voting) initialized by regularized spectral clusteringConsensusClust
Estimates DCSBM modelDCSBM.estimate
selecting block models by ECVECV.block
selecting tuning parameter for neighborhood smoothing estimation of graphon modelECV.nSmooth.lowrank
estimates optimal low rank model for a networkECV.Rank
identify the informative core component of a networkInformativeCore
identify the K-core component of a networkk.core
selecting number of communities by asymptotic likelihood ratioLRBIC
estimates inner product latent space model by projected gradient descentLSM.PGD
selecting block models by NCVNCV.select
estimates network connection probability by network mixingnetwork.mixing
estimates network connection probability by network mixing with B-fold averagingnetwork.mixing.Bfold
calculates normalized mutual informationNMI
estimates nomination SBM parameters given community labels by the method of momentsNSBM.estimate
Generates networks from nomination stochastic block modelNSBM.Gen
estimates probabilty matrix by neighborhood smoothingnSmooth
generates random networks from random dot product graph modelRDPG.Gen
clusters nodes by regularized spectral clusteringreg.SP
detects communities by regularized spherical spectral clusteringreg.SSP
clusters nodes in a directed network by regularized spectral clustering on right singular vectorsRightSC
estimates SBM parameters given community labelsSBM.estimate
oracle smooth graphon estimationsmooth.oracle
estimates the network probability matrix by the improved universal singular value thresholdingUSVT