Generate random positions (latitude/longitude),
Well-known text ('WKT') points or polygons, or 'GeoJSON' points or
polygons.
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com"),
person("Noam", "Ross", role = "aut")
)
Scott Chamberlain [aut, cre],
Noam Ross [aut]

c(person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com", comment = c(ORCID = "0000-0003-1444-9135")),
person("Noam", "Ross", role = "aut"),
person("Samuel", "Bosch", role = "aut")
)
Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>),
Noam Ross [aut],
Samuel Bosch [aut]

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>).
methods, stats, poweRlaw, RSpectra, irlba

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> and the method of moments estimation of nomination SBM of Li et. al. (2020) <arxiv:2008.03652>.