Stochastic collapsed variational inference on mixed-membership stochastic blockmodel for networks, incorporating node-level predictors of mixed-membership vectors, as well as dyad-level predictors. For networks observed over time, the model defines a hidden Markov process that allows the effects of node-level predictors to evolve in discrete, historical periods. In addition, the package offers a variety of utilities for exploring results of estimation, including tools for conducting posterior predictive checks of goodness-of-fit and several plotting functions. The package implements methods described in Olivella, Pratt and Imai (2019) 'Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Conflicts', available at <https://www.santiagoolivella.info/pdfs/socnet.pdf>.
|Author||Santiago Olivella [aut, cre], Adeline Lo [aut, cre], Tyler Pratt [aut, cre], Kosuke Imai [aut, cre]|
|Maintainer||Santiago Olivella <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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