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