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  "Title": "Dynamic Mixed-Membership Network Regression Model",
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  "Author": "Santiago Olivella [aut, cre], Adeline Lo [aut, cre], Tyler\nPratt [aut, cre], Kosuke Imai [aut, cre]",
  "Maintainer": "Santiago Olivella <olivella@unc.edu>",
  "Description": "Stochastic collapsed variational inference on\nmixed-membership stochastic blockmodel for networks,\nincorporating node-level predictors of mixed-membership\nvectors, as well as dyad-level predictors. For networks\nobserved over time, the model defines a hidden Markov process\nthat allows the effects of node-level predictors to evolve in\ndiscrete, historical periods. In addition, the package offers a\nvariety of utilities for exploring results of estimation,\nincluding tools for conducting posterior predictive checks of\ngoodness-of-fit and several plotting functions. The package\nimplements methods described in Olivella, Pratt and Imai (2019)\n'Dynamic Stochastic Blockmodel Regression for Social Networks:\nApplication to International Conflicts', available at\n<https://www.santiagoolivella.info/pdfs/socnet.pdf>.",
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      "version": "0.2.0.2",
      "date": "2026-05-22T09:23:06.000Z",
      "arch": "emscripten",
      "commit": "627b94ab50e5298f82e5e1e1e211f1b1ac8fb394",
      "fileid": "9e0b97d03f230994913cf46888d4c9bd60f0782df0d29dea3f120f4d1f978736",
      "status": "success",
      "buildurl": "https://github.com/r-universe/solivella/actions/runs/25844720815"
    }
  ]
}