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 include the informative core-periphery data processing method of Miao and Lu (2021) <arXiv:2101.06388>. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.

Version: 0.5
Depends: Matrix, entropy, AUC
Imports: methods, stats, poweRlaw, RSpectra, irlba, pracma, nnls
Published: 2022-01-04
Author: Tianxi Li, Elizaveta Levina, Ji Zhu, Can M. Le
Maintainer: Tianxi Li <tianxili at virginia.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: randnet results


Reference manual: randnet.pdf


Package source: randnet_0.5.tar.gz
Windows binaries: r-devel: randnet_0.5.zip, r-release: randnet_0.5.zip, r-oldrel: randnet_0.5.zip
macOS binaries: r-release (arm64): randnet_0.5.tgz, r-release (x86_64): randnet_0.5.tgz, r-oldrel: randnet_0.5.tgz
Old sources: randnet archive

Reverse dependencies:

Reverse imports: HCD, multiviewtest, NetworkReg


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