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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).
Getting started
Browse package contents
Package details |
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| Author | Carter Allen [aut, cre] (<https://orcid.org/0000-0001-6937-7234>), Dongjun Chung [aut] |
| Maintainer | Carter Allen <carter.allen12@gmail.com> |
| License | GPL (>= 2) |
| Version | 0.99.2 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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