Package: randnet
Type: Package
Title: Random Network Model Estimation, Selection and Parameter Tuning
Version: 0.5
Date: 2022-01-01
Author: Tianxi Li, Elizaveta Levina, Ji Zhu, Can M. Le
Maintainer: Tianxi Li <tianxili@virginia.edu>
Description: 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.
License: GPL (>= 2)
Depends: Matrix, entropy, AUC
Imports: methods, stats, poweRlaw, RSpectra, irlba,pracma,nnls
NeedsCompilation: no
Packaged: 2022-01-02 15:33:46 UTC; tianxili
Repository: CRAN
Date/Publication: 2022-01-04 00:20:02 UTC
Built: R 4.0.5; ; 2022-01-25 01:08:41 UTC; unix
