Package: randnet 0.7

Tianxi Li

randnet: Random Network Model Estimation, Selection and Parameter Tuning

Model selection and parameter tuning procedures for a class of random network models. The model selection can be done by a general cross-validation framework called ECV from Li et. al. (2016) <arxiv:1612.04717> . Several other model-based and task-specific methods are also included, such as NCV from Chen and Lei (2016) <arxiv:1411.1715>, likelihood ratio method from Wang and Bickel (2015) <arxiv:1502.02069>, spectral methods from Le and Levina (2015) <arxiv:1507.00827>. Many network analysis methods are also implemented, such as the regularized spectral clustering (Amini et. al. 2013 <doi:10.1214/13-AOS1138>) and its degree corrected version and graphon neighborhood smoothing (Zhang et. al. 2015 <arxiv:1509.08588>). It also includes the consensus clustering of Gao et. al. (2014) <arxiv:1410.5837>, the method of moments estimation of nomination SBM of Li et. al. (2020) <arxiv:2008.03652>, and the network mixing method of Li and Le (2021) <arxiv:2106.02803>. It also includes the informative core-periphery data processing method of Miao and Li (2021) <arxiv:2101.06388>. 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]

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24 exports 1.00 score 68 dependencies 2 dependents 1 mentions 31 scripts 383 downloads

Last updated 1 years agofrom:9dcbcd5ef7. Checks:OK: 6 ERROR: 1. Indexed: yes.

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

Dependencies:abindashAUCbitopsbootcliclustercolorspacedata.tabledeSolveentropyfansifarverfdafdsFNNgamm4ggplot2gluegrpreggtablehdrcdeirlbaisobandkernlabKernSmoothkslabelinglatticelifecyclelme4locfitmagicmagrittrMASSMatrixmclustmgcvminqamulticoolmunsellmvtnormnlmenloptrnnlspbspcaPPpillarpkgconfigpoweRlawpracmaR6rainbowRColorBrewerRcppRcppEigenRCurlrefundrlangRLRsimRSpectrascalessparseFLMMtibbleutf8vctrsviridisLitewithr