quickPlot (>= 0.1.2), sf, sp, SpaDES.tools, stats

person("Alex M", "Chubaty", email = "alex.chubaty@gmail.com",

Author: Sarah Bauduin [aut, cre] (<https://orcid.org/0000-0002-3252-5894>),
Eliot J B McIntire [aut] (<https://orcid.org/0000-0002-6914-8316>),

Title: Dynamic Mixed-Membership Network Regression Model
Description: 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. The package
implements methods described in Olivella, Pratt and Imai (2019) ''Dynamic Stochastic
Blockmodel Regression for Social Networks: Application to International Conflicts'',

implements methods described in Olivella, Pratt and Imai (2019) ``Dynamic Stochastic

Description: Stochastic collapsed variational inference on mixed-membership stochastic blockmodel for networks,
implements methods described in Olivella, Pratt and Imai (2019) 'Dynamic Stochastic
Blockmodel Regression for Social Networks: Application to International Conflicts',

Title: Origin Estimation for Propagation Processes on Complex Networks
Description: Performs network-based source estimation. Different approaches are available: effective distance median, recursive backtracking, and centrality-based source estimation. Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.
Authors@R: c(person("Juliane", "Manitz", email="r@manitz.org", role=c("aut", "cre")), person("Jonas", "Harbering", email="", role=c("ctb")))
Author: Juliane Manitz [aut, cre], Jonas Harbering [ctb]