randnet: Random Network Model 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>).

Version: 0.1
Depends: Matrix, entropy, AUC
Imports: methods, stats, poweRlaw, RSpectra, irlba
Published: 2017-09-17
Author: Tianxi Li, Elizaveta Levina, Ji Zhu
Maintainer: Tianxi Li <tianxili at umich.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: randnet results

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Reference manual: randnet.pdf
Package source: randnet_0.1.tar.gz
Windows binaries: r-prerel: randnet_0.1.zip, r-release: randnet_0.1.zip, r-oldrel: randnet_0.1.zip
OS X binaries: r-prerel: randnet_0.1.tgz, r-release: randnet_0.1.tgz

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