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URL: https://CRAN.R-project.org/package=mlsbm
Proper Citation: MLSBM (RRID:SCR_021795)
Description: Software R package to Fit Bayesian stochastic block models and multi level stochastic block models using efficient Gibbs sampling implemented in Rcpp. Models assume symmetric, non reflexive graphs with unweighted, binary edges. Data are input as symmetric binary adjacency matrix, or list of such matrices.
Synonyms: Efficient Estimation of Bayesian SBMs and MLSBMs
Resource Type: software resource, software toolkit
Keywords: Fit Bayesian stochastic block models, multi level stochastic block models, Gibbs sampling, Rcpp
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Source: SciCrunch Registry
Data set from Kravitz, Owen and Kretizer's 2012 paper "Optogenetic identification of striatal projection neuron subtypes during in vivo recordings."
