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.
||Matrix, entropy, AUC, sparseFLMM, mgcv
||methods, stats, poweRlaw, RSpectra, irlba, pracma, nnls, data.table
||Tianxi Li [aut, cre],
Elizeveta Levina [aut],
Ji Zhu [aut],
Can M. Le [aut]
||Tianxi Li <tianxili at virginia.edu>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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