{
  "_id": "6a1030afacfb0bcc41c95c57",
  "Package": "missSBM",
  "Type": "Package",
  "Title": "Handling Missing Data in Stochastic Block Models",
  "Version": "1.0.5",
  "Authors@R": "c(\nperson(\"Julien\", \"Chiquet\", role = c(\"aut\", \"cre\"), email = \"julien.chiquet@inrae.fr\",\ncomment = c(ORCID = \"0000-0002-3629-3429\")),\nperson(\"Pierre\", \"Barbillon\", role = \"aut\", email = \"pierre.barbillon@agroparistech.fr\",\ncomment = c(ORCID = \"0000-0002-7766-7693\")),\nperson(\"Timothée\", \"Tabouy\", role = \"aut\", email = \"timothee.tabouy@agroparistech.fr\"),\nperson(\"Jean-Benoist\", \"Léger\", role = \"ctb\", email = \"jbleger@hds.utc.fr\", comment = \"provided C++ implementaion of K-means\"),\nperson(\"François\", \"Gindraud\", role = \"ctb\", email = \"francois.gindraud@gmail.com\", comment = \"provided C++ interface to NLopt\"),\nperson(\"großBM team\", role = c(\"ctb\"))\n)",
  "Maintainer": "Julien Chiquet <julien.chiquet@inrae.fr>",
  "Description": "When a network is partially observed (here, NAs in the\nadjacency matrix rather than 1 or 0 due to missing information\nbetween node pairs), it is possible to account for the\nunderlying process that generates those NAs. 'missSBM',\npresented in 'Barbillon, Chiquet and Tabouy' (2022)\n<doi:10.18637/jss.v101.i12>, adjusts the popular stochastic\nblock model from network data sampled under various missing\ndata conditions, as described in 'Tabouy, Barbillon and\nChiquet' (2019) <doi:10.1080/01621459.2018.1562934>.",
  "URL": "https://grosssbm.github.io/missSBM/",
  "BugReports": "https://github.com/grossSBM/missSBM/issues",
  "License": "GPL-3",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.2",
  "Collate": "'utils_missSBM.R' 'R6Class-networkSampling.R'\n'R6Class-networkSampling_fit.R' 'R6Class-simpleSBM_fit.R'\n'R6Class-missSBM_fit.R' 'R6Class-missSBM_collection.R'\n'R6Class-networkSampler.R' 'R6Class-partlyObservedNetwork.R'\n'RcppExports.R' 'er_network.R' 'estimateMissSBM.R'\n'frenchblog2007.R' 'kmeans.R' 'missSBM-package.R'\n'observeNetwork.R' 'war.R'",
  "VignetteBuilder": "knitr",
  "Language": "en-US",
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  "Repository": "https://grosssbm.r-universe.dev",
  "Date/Publication": "2025-03-13 09:02:21 UTC",
  "RemoteUrl": "https://github.com/grosssbm/misssbm",
  "RemoteRef": "HEAD",
  "RemoteSha": "edc94376f1b2d15ff5e6f9caf2b6b08c2cae31db",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-05-12 09:24:35 UTC",
    "User": "root"
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  "Author": "Julien Chiquet [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-3629-3429>),\nPierre Barbillon [aut] (ORCID: <https://orcid.org/0000-0002-7766-7693>),\nTimothée Tabouy [aut],\nJean-Benoist Léger [ctb] (provided C++ implementaion of K-means),\nFrançois Gindraud [ctb] (provided C++ interface to NLopt),\ngroßBM team [ctb]",
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  "_user": "grosssbm",
  "_type": "src",
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  "_created": "2026-05-12T09:24:35.000Z",
  "_published": "2026-05-22T10:32:15.308Z",
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    "message": "last CRAN submission\n",
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  "_tags": [],
  "_topics": [
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    "nas",
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    "description": "A team to build damn outstanding packages to adjust a variety of stochastic blockmodels"
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  "_devurl": "https://github.com/grosssbm/misssbm",
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      "date": "2019-06-08"
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      "version": "0.2.1",
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    },
    {
      "version": "0.3.0",
      "date": "2020-11-18"
    },
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      "version": "1.0.0",
      "date": "2021-05-25"
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      "date": "2023-10-24"
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    "%>%",
    "estimateMissSBM",
    "l1_similarity",
    "missSBM_collection",
    "missSBM_fit",
    "observeNetwork"
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      "title": "ER ego centered network",
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      "class": [
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      ],
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      "table": false,
      "tojson": false
    },
    {
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      "title": "Political Blogosphere network prior to 2007 French presidential election",
      "object": "frenchblog2007",
      "class": [
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      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
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      "class": [
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      "fields": [],
      "table": false,
      "tojson": false
    }
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  "_help": [
    {
      "page": "blockDyadSampler",
      "title": "Class for defining a block dyad sampler",
      "topics": [
        "blockDyadSampler"
      ]
    },
    {
      "page": "blockDyadSampling_fit",
      "title": "Class for fitting a block-dyad sampling",
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    },
    {
      "page": "blockNodeSampler",
      "title": "Class for defining a block node sampler",
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    },
    {
      "page": "blockNodeSampling_fit",
      "title": "Class for fitting a block-node sampling",
      "topics": [
        "blockNodeSampling_fit"
      ]
    },
    {
      "page": "coef.missSBM_fit",
      "title": "Extract model coefficients",
      "topics": [
        "coef.missSBM_fit"
      ]
    },
    {
      "page": "covarDyadSampling_fit",
      "title": "Class for fitting a dyad sampling with covariates",
      "topics": [
        "covarDyadSampling_fit"
      ]
    },
    {
      "page": "covarNodeSampling_fit",
      "title": "Class for fitting a node-centered sampling with covariate",
      "topics": [
        "covarNodeSampling_fit"
      ]
    },
    {
      "page": "degreeSampler",
      "title": "Class for defining a degree sampler",
      "topics": [
        "degreeSampler"
      ]
    },
    {
      "page": "degreeSampling_fit",
      "title": "Class for fitting a degree sampling",
      "topics": [
        "degreeSampling_fit"
      ]
    },
    {
      "page": "doubleStandardSampler",
      "title": "Class for defining a double-standard sampler",
      "topics": [
        "doubleStandardSampler"
      ]
    },
    {
      "page": "doubleStandardSampling_fit",
      "title": "Class for fitting a double-standard sampling",
      "topics": [
        "doubleStandardSampling_fit"
      ]
    },
    {
      "page": "dyadSampler",
      "title": "Virtual class for all dyad-centered samplers",
      "topics": [
        "dyadSampler"
      ]
    },
    {
      "page": "dyadSampling_fit",
      "title": "Class for fitting a dyad sampling",
      "topics": [
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      ]
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    {
      "page": "er_network",
      "title": "ER ego centered network",
      "topics": [
        "er_network"
      ]
    },
    {
      "page": "estimateMissSBM",
      "title": "Estimation of simple SBMs with missing data",
      "topics": [
        "estimateMissSBM"
      ]
    },
    {
      "page": "fitted.missSBM_fit",
      "title": "Extract model fitted values from object 'missSBM_fit', return by 'estimateMissSBM()'",
      "topics": [
        "fitted.missSBM_fit"
      ]
    },
    {
      "page": "frenchblog2007",
      "title": "Political Blogosphere network prior to 2007 French presidential election",
      "topics": [
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    },
    {
      "page": "l1_similarity",
      "title": "L1-similarity",
      "topics": [
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      ]
    },
    {
      "page": "missSBM_collection",
      "title": "An R6 class to represent a collection of SBM fits with missing data",
      "topics": [
        "missSBM_collection"
      ]
    },
    {
      "page": "missSBM_fit",
      "title": "An R6 class to represent an SBM fit with missing data",
      "topics": [
        "missSBM_fit"
      ]
    },
    {
      "page": "networkSampler",
      "title": "Definition of R6 Class 'networkSampling_sampler'",
      "topics": [
        "networkSampler"
      ]
    },
    {
      "page": "networkSampling",
      "title": "Definition of R6 Class 'networkSampling'",
      "topics": [
        "networkSampling"
      ]
    },
    {
      "page": "networkSamplingDyads_fit",
      "title": "Virtual class used to define a family of networkSamplingDyads_fit",
      "topics": [
        "networkSamplingDyads_fit"
      ]
    },
    {
      "page": "networkSamplingNodes_fit",
      "title": "Virtual class used to define a family of networkSamplingNodes_fit",
      "topics": [
        "networkSamplingNodes_fit"
      ]
    },
    {
      "page": "nodeSampler",
      "title": "Virtual class for all node-centered samplers",
      "topics": [
        "nodeSampler"
      ]
    },
    {
      "page": "nodeSampling_fit",
      "title": "Class for fitting a node sampling",
      "topics": [
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    {
      "page": "observeNetwork",
      "title": "Observe a network partially according to a given sampling design",
      "topics": [
        "observeNetwork"
      ]
    },
    {
      "page": "partlyObservedNetwork",
      "title": "An R6 Class used for internal representation of a partially observed network",
      "topics": [
        "partlyObservedNetwork"
      ]
    },
    {
      "page": "plot.missSBM_fit",
      "title": "Visualization for an object 'missSBM_fit'",
      "topics": [
        "plot.missSBM_fit"
      ]
    },
    {
      "page": "predicted.missSBM_fit",
      "title": "Prediction of a 'missSBM_fit' (i.e. network with imputed missing dyads)",
      "topics": [
        "predict.missSBM_fit",
        "predicted.missSBM_fit"
      ]
    },
    {
      "page": "simpleDyadSampler",
      "title": "Class for defining a simple dyad sampler",
      "topics": [
        "simpleDyadSampler"
      ]
    },
    {
      "page": "simpleNodeSampler",
      "title": "Class for defining a simple node sampler",
      "topics": [
        "simpleNodeSampler"
      ]
    },
    {
      "page": "SimpleSBM_fit",
      "title": "This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.",
      "topics": [
        "SimpleSBM_fit"
      ]
    },
    {
      "page": "SimpleSBM_fit_MNAR",
      "title": "This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.",
      "topics": [
        "SimpleSBM_fit_MNAR"
      ]
    },
    {
      "page": "SimpleSBM_fit_noCov",
      "title": "This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.",
      "topics": [
        "SimpleSBM_fit_noCov"
      ]
    },
    {
      "page": "SimpleSBM_fit_withCov",
      "title": "This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.",
      "topics": [
        "SimpleSBM_fit_withCov"
      ]
    },
    {
      "page": "snowballSampler",
      "title": "Class for defining a snowball sampler",
      "topics": [
        "snowballSampler"
      ]
    },
    {
      "page": "summary.missSBM_fit",
      "title": "Summary method for a 'missSBM_fit'",
      "topics": [
        "summary.missSBM_fit"
      ]
    },
    {
      "page": "war",
      "title": "War data set",
      "topics": [
        "war"
      ]
    }
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  "_readme": "https://github.com/grosssbm/misssbm/raw/HEAD/README.md",
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  "_vignettes": [
    {
      "source": "case_study_war_networks.Rmd",
      "filename": "case_study_war_networks.html",
      "title": "missSBM: a case study with war networks",
      "author": "missSBM team",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Prerequisites",
        "The war network",
        "Generating missing data",
        "Estimation with missing data",
        "Estimation on fully observed network",
        "Taking covariates into account",
        "Military power",
        "Trade data",
        "References"
      ],
      "created": "2019-04-12 09:39:27",
      "modified": "2023-10-24 10:11:24",
      "commits": 35
    }
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  "_universes": [
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