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NetMix

NetMix: Dynamic Mixed-Membership Network Regression Model. Variational EM estimation of 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.

Installation

To install the latest CRAN version, copy and paste the following in a running R console: install.packages("NetMix").

For the latest development version, copy and paste the following (assuming you have the remotes package installed): remotes::install_github("solivella/NetMix")

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Version

Install

install.packages('NetMix')

Monthly Downloads

274

Version

0.2.0.3

License

GPL (>= 2)

Maintainer

Santiago Olivella

Last Published

June 5th, 2025

Functions in NetMix (0.2.0.3)

simulate.mmsbm

Simulate a complete sociomatrix from an mmsbm object
predict.mmsbm

Predict edges based on estimated mmsbm model
summary.mmsbm

Summarize 'mmsbm' object
vcov.mmsbm

Extract Variance-Covariance Matrix for a Fitted mmsbm Object
plot.mmsbm

Various visualization tools for 'mmsbm' objects
approxB

Internal functions and generics for mmsbm package
lazega_dyadic

Dyadic predictors in the Lazega friendship network (Lazega 2001).
covFX

Generate estimated monadic covariate effects for estimated mmsbm model
coef.mmsbm

Extract Regression Coefficients for a Fitted mmsbm Object
mmsbm_fit

Fitter Function for dynamic MMSBM Model
head.mmsbm

Identify nodes with most frequent membership in latent groups
gof

Posterior predictive checks using structural network charactericts
mmsbm

Dynamic mixed-membership stochastic blockmodel with covariates
lazega_monadic

Monadic predictors in the Lazega friendship network (Lazega 2001).