NetMix: Dynamic Mixed-Membership Network Regression Model

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>.

Getting started

Package details

AuthorSantiago Olivella [aut, cre], Adeline Lo [aut, cre], Tyler Pratt [aut, cre], Kosuke Imai [aut, cre]
MaintainerSantiago Olivella <olivella@unc.edu>
LicenseGPL (>= 2)
Version0.2.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("NetMix")

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NetMix documentation built on March 2, 2021, 1:07 a.m.