blockmodels

R package blockmodels: Latent and Stochastic Block Model Estimation by a ’V-EM’ Algorithm. Latent and Stochastic Block Model estimation by a Variational EM algorithm. Various probability distribution are provided (Bernoulli, Poisson...), with or without covariates.

Keywords for this software

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References in zbMATH (referenced in 7 articles , 1 standard article )

Showing results 1 to 7 of 7.
Sorted by year (citations)

  1. Etienne Côme, Nicolas Jouvin : greed: An R Package for Model-Based Clustering by Greedy Maximization of the Integrated Classification Likelihood (2022) arXiv
  2. Green, Alden; Shalizi, Cosma Rohilla: Bootstrapping exchangeable random graphs (2022)
  3. Marina Knight, Kathryn Leeming, Guy Nason, Matthew Nunes: Generalized Network Autoregressive Processes and the GNAR Package (2020) not zbMATH
  4. Ranciati, Saverio; Vinciotti, Veronica; Wit, Ernst C.: Identifying overlapping terrorist cells from the Noordin Top actor-event network (2020)
  5. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  6. Julien Chiquet, Pierre Barbillon, Timothée Tabouy: missSBM: An R Package for Handling Missing Values in the Stochastic Block Model (2019) arXiv
  7. Jean-Benoist Leger: Blockmodels: A R-package for estimating in Latent Block Model and Stochastic Block Model, with various probability functions, with or without covariates (2016) arXiv