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>.

Origin Estimation for Propagation Processes on Complex Networks
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.
c(person("Juliane", "Manitz", email="r@manitz.org", role=c("aut", "cre")), person("Jonas", "Harbering", email="", role=c("ctb")))
'0_helper_net.r' 'NetOrigin.r' 'origin_helper.r'
'origin_methods.r' 'distance.r' 'data.r' 'data_handling.r'
'robustness.r'
Juliane Manitz [aut, cre], Jonas Harbering [ctb]

Juliane Manitz [aut, cre],
Jonas Harbering [ctb]

c(person("Juliane", "Manitz", email="r@manitz.org", role=c("aut", "cre")), person("Jonas", "Harbering", email="", role=c("ctb")), person("Jun", "Li", email="", role=c("ctb")))
igraph, Hmisc, colorspace, mvtnorm, corpcor
'0_helper_net.r' 'NetOrigin.r' 'compute_mu_lambda.R'
'origin_helper.r' 'origin_methods.r' 'distance.r' 'data.r'
'data_handling.r' 'initial_condition_sib_model.R'
'robustness.r' 'stochastic_sib_model.R'
Juliane Manitz [aut, cre],
Jonas Harbering [ctb],
Jun Li [ctb]