{
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  "Package": "mixedMem",
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  "Title": "Tools for Discrete Multivariate Mixed Membership Models",
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  "Date": "2020-11-28",
  "Authors@R": "c( person(\"Y. Samuel\", \"Wang\", email = \"ysamuelwang@gmail.com\", role =\nc(\"aut\",\"cre\")), person(\"Elena A.\", \"Erosheva\", email =\n\"erosheva@uw.edu\", role = \"aut\"))",
  "Maintainer": "Y. Samuel Wang <ysamuelwang@gmail.com>",
  "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 uses a\nVariational EM approach by approximating the posterior\ndistribution of latent memberships and selecting\nhyperparameters through a pseudo-MLE procedure. 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.",
  "License": "GPL (>= 2)",
  "LazyData": "TRUE",
  "VignetteBuilder": "knitr",
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  "Packaged": {
    "Date": "2026-06-09 09:55:50 UTC",
    "User": "root"
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  "Author": "Y. Samuel Wang [aut, cre], Elena A. Erosheva [aut]",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2020-12-01 16:30:13 UTC",
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    "permuteLabels",
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      "title": "Responses from 1983 American National Election Survey Pilot",
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  "_help": [
    {
      "page": "mixedMem-package",
      "title": "Tools for fitting discrete multivariate mixed membership models",
      "concept": [
        "mixed membership",
        "grade of membership",
        "variational inference"
      ],
      "topics": [
        "mixedMem-package",
        "mixedMem"
      ]
    },
    {
      "page": "ANES",
      "title": "Responses from 1983 American National Election Survey Pilot",
      "topics": [
        "ANES"
      ]
    },
    {
      "page": "computeBIC",
      "title": "Compute the approximate BIC",
      "topics": [
        "computeBIC"
      ]
    },
    {
      "page": "computeELBO",
      "title": "Compute a lower bound on the log-likelihood (ELBO)",
      "topics": [
        "computeELBO"
      ]
    },
    {
      "page": "findLabels",
      "title": "Mixed Membership Post-Processing",
      "topics": [
        "findLabels"
      ]
    },
    {
      "page": "gmv_theta",
      "title": "Point estimates from Gross and Manrique-Vallier 2014",
      "topics": [
        "gmv_theta"
      ]
    },
    {
      "page": "mixedMemModel",
      "title": "Constructor for a Mixed Membership Model Object",
      "topics": [
        "mixedMemModel"
      ]
    },
    {
      "page": "mmVarFit",
      "title": "Fit Mixed Membership models using variational EM",
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      ]
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      "title": "Mixed Membership Post-Processing",
      "topics": [
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    {
      "page": "plot.mixedMemModel",
      "title": "Plot a Mixed Membership Model",
      "topics": [
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      ]
    },
    {
      "page": "rmixedMem",
      "title": "Simulate Mixed Membership Data",
      "topics": [
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    {
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      "title": "Summary of a Mixed Membership Model",
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      "title": "Mixed Membership Visualization",
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