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).


Reference manual

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0.99.2 by Carter Allen, a year 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