NetMix

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.

Last 30 days

This package has been downloaded 181 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 2,680 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Sep 11, 2024 with 37 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.