c(
person("Sarah", "Bauduin", email = "sarahbauduin@hotmail.fr",
role = c("aut"), comment = c(ORCID = "0000-0002-3252-5894")),
person("Eliot J B", "McIntire", email = "eliot.mcintire@canada.ca",
role = c("aut", "cre"), comment=c(ORCID = "0000-0002-6914-8316")),
person("Alex M", "Chubaty", email = "alex.chubaty@gmail.com",
role = c("aut"), comment = c(ORCID = "0000-0001-7146-8135")),
person("Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada",
role = "cph")
)

'Agent-classes.R' 'NetLogoR-package.R'
'worldNLR-classes-methods.R' 'agentMatrix-Class-methods.R'
'agentset-functions.R' 'function-arguments.R'
'patch-functions.R' 'plot.R' 'world-functions.R' 'quickPlot.R'
'spades-functions.R' 'turtle-functions.R'

Dynamic Mixed-Membership Network Regression Model
Kosuke Imai [aut, cre],
Tyler Pratt [aut, cre],
Adeline Lo [aut, cre],
Santiago Olivella [aut, cre]
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'',
available at <http://santiagoolivella.info/wp-content/uploads/2018/07/dSBM_Reg.pdf>.

Santiago Olivella [aut, cre],
Adeline Lo [aut, cre],
Tyler Pratt [aut, cre],
Kosuke Imai [aut, cre]
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'',
available at <http://santiagoolivella.info/wp-content/uploads/2018/07/dSBM_Reg.pdf>.