randnet

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) doi:10.48550/arXiv.1612.04717. Several other model-based and task-specific methods are also included, such as NCV from Chen and Lei (2016) doi:10.48550/arXiv.1411.1715, likelihood ratio method from Wang and Bickel (2015) doi:10.48550/arXiv.1502.02069, spectral methods from Le and Levina (2015) doi:10.48550/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 doi:10.48550/arXiv.1509.08588). It also includes the consensus clustering of Gao et. al. (2014) doi:10.48550/arXiv.1410.5837, the method of moments estimation of nomination SBM of Li et. al. (2020) doi:10.48550/arXiv.2008.03652, and the network mixing method of Li and Le (2021) doi:10.48550/arXiv.2106.02803. It also includes the informative core-periphery data processing method of Miao and Li (2021) doi:10.48550/arXiv.2101.06388. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.

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