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mlsbm  

Efficient Estimation of Bayesian SBMs & MLSBMs
View on CRAN: Click here


Download and install mlsbm package within the R console
Install from CRAN:
install.packages("mlsbm")

Install from Github:
library("remotes")
install_github("cran/mlsbm")

Install by package version:
library("remotes")
install_version("mlsbm", "0.99.2")



Attach the package and use:
library("mlsbm")
Maintained by
Carter Allen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-02-07
Latest Update: 2021-02-07
Description:
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).
How to cite:
Carter Allen (2021). mlsbm: Efficient Estimation of Bayesian SBMs & MLSBMs. R package version 0.99.2, https://cran.r-project.org/web/packages/mlsbm. Accessed 22 Dec. 2024.
Previous versions and publish date:
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Complete documentation for mlsbm
Functions, R codes and Examples using the mlsbm R package
Some associated functions: AL . col_summarize . fit_mlsbm . fit_sbm . mean_CRI . mlsbm . sample_mlsbm . sample_sbm . 
Some associated R codes: AL.R . RcppExports.R . col_summarize.R . fit_mlsbm.R . fit_sbm.R . mean_CRI.R . mlsbm.R . sample_mlsbm.R . sample_sbm.R .  Full mlsbm package functions and examples
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