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mlsbm

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).

Versions across snapshots

VersionRepositoryFileSize
0.99.2 rolling linux/jammy R-4.5 mlsbm_0.99.2.tar.gz 108.4 KiB
0.99.2 rolling linux/noble R-4.5 mlsbm_0.99.2.tar.gz 110.2 KiB
0.99.2 rolling source/ R- mlsbm_0.99.2.tar.gz 12.4 KiB
0.99.2 latest linux/jammy R-4.5 mlsbm_0.99.2.tar.gz 108.4 KiB
0.99.2 latest linux/noble R-4.5 mlsbm_0.99.2.tar.gz 110.2 KiB
0.99.2 latest source/ R- mlsbm_0.99.2.tar.gz 12.4 KiB
0.99.2 2026-04-26 source/ R- mlsbm_0.99.2.tar.gz 12.4 KiB
0.99.2 2026-04-23 source/ R- mlsbm_0.99.2.tar.gz 12.4 KiB
0.99.2 2026-04-09 windows/windows R-4.5 mlsbm_0.99.2.zip 429.4 KiB
0.99.2 2025-04-20 source/ R- mlsbm_0.99.2.tar.gz 12.4 KiB

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