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  "Title": "Tools for Discrete Multivariate Mixed Membership Models",
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  "Authors@R": "c( person(\"Y. Samuel\", \"Wang\", email = \"ysamwang@uw.edu\", role =\nc(\"aut\",\"cre\")), person(\"Elena A.\", \"Erosheva\", email =\n\"erosheva@uw.edu\", role = \"aut\"))",
  "Maintainer": "Y. Samuel Wang <ysamwang@uw.edu>",
  "Description": "Fits mixed membership models with discrete multivariate\ndata (with or without repeated measures) following the general\nframework of Erosheva et al (2004). This package includes a\nVariational EM and MCMC estimation approaches. Currently\nsupported data types are Bernoulli, multinomial and rank\n(Plackett-Luce). The extended GoM model with fixed stayers from\nErosheva et al (2007) is now also supported. See Airoldi et al\n(2014) for other examples of mixed membership models.",
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  "Repository": "https://ysamwang.r-universe.dev",
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      "page": "mixedMem-package",
      "title": "Tools for fitting discrete multivariate mixed membership models",
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        "mixedMem"
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    },
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      "page": "ANES",
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      "topics": [
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    },
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      "title": "Compute the approximate BIC",
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