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>.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.2.0 by Santiago Olivella, 8 months ago

Report a bug at https://github.com/solivella/NetMix/issues

Browse source code at https://github.com/cran/NetMix

Authors: Santiago Olivella [aut, cre] , Adeline Lo [aut, cre] , Tyler Pratt [aut, cre] , Kosuke Imai [aut, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports clue, graphics, grDevices, gtools, igraph, lda, Matrix, MASS, methods, poisbinom, Rcpp, stats, utils

Suggests ergm, ggplot2, network, scales

Linking to Rcpp, RcppArmadillo

System requirements: C++11

See at CRAN