Efficient Estimation of Bayesian SBMs & MLSBMs

Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs).


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install.packages("mlsbm")

0.99.2 by Carter Allen, 25 days ago


Browse source code at https://github.com/cran/mlsbm


Authors: Carter Allen [aut, cre] , Dongjun Chung [aut]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Rcpp

Linking to Rcpp, RcppArmadillo


See at CRAN