{
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  "Title": "Fitting Shared Atoms Nested Models via Variational Bayes",
  "Version": "0.1.1",
  "Date": "2024-05-14",
  "Authors@R": "c(\nperson(\"Francesco\", \"Denti\", ,\"francescodenti.personal@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0001-5034-7414\")),\nperson(\"Laura\", \"D'Angelo\", ,\"laura.dangelo@live.com\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0000-0003-2978-4702\"))\n)",
  "Maintainer": "Francesco Denti <francescodenti.personal@gmail.com>",
  "URL": "https://github.com/fradenti/SANvi",
  "BugReports": "https://github.com/fradenti/SANvi/issues",
  "Description": "An efficient tool for fitting the nested common and shared\natoms models using variational Bayes approximate inference for\nfast computation. Specifically, the package implements the\ncommon atoms model (Denti et al., 2023), its finite version\n(D'Angelo et al., 2023), and a hybrid finite-infinite model.\nAll models use Gaussian mixtures with a normal-inverse-gamma\nprior distribution on the parameters. Additional functions are\nprovided to help analyze the results of the fitting procedure.\nReferences: Denti, Camerlenghi, Guindani, Mira (2023)\n<doi:10.1080/01621459.2021.1933499>, D’Angelo, Canale, Yu,\nGuindani (2023) <doi:10.1111/biom.13626>.",
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  "NeedsCompilation": "yes",
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    "Date": "2026-06-08 10:24:11 UTC",
    "User": "root"
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  "Author": "Francesco Denti [aut, cre, cph]\n(<https://orcid.org/0000-0001-5034-7414>), Laura D'Angelo [aut]\n(<https://orcid.org/0000-0003-2978-4702>)",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2024-05-16 02:38:43 UTC",
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    "estimate_clustering_vi",
    "extract_best",
    "variational_CAM",
    "variational_fiSAN",
    "variational_fSAN",
    "variational_multistart"
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      "page": "estimate_atoms_weights_vi",
      "title": "Estimate the Posterior Atoms and Weights of the Discrete Mixing Distributions",
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        "plot.vi_atoms_weights",
        "print.vi_atoms_weights"
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      "title": "Estimate Posterior Clustering Assignments",
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        "plot.vi_clustering",
        "print.vi_clustering"
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      "title": "Extract the best run from multiple trials",
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    {
      "page": "plot.SANvb",
      "title": "Plotting the variational inference output",
      "topics": [
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      ]
    },
    {
      "page": "print.SANvb",
      "title": "Print variational inference output",
      "topics": [
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    {
      "page": "variational_CAM",
      "title": "Mean Field Variational Bayes estimation of CAM",
      "topics": [
        "variational_CAM"
      ]
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      "page": "variational_fiSAN",
      "title": "Mean Field Variational Bayes estimation of fiSAN",
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        "variational_fiSAN"
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    },
    {
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      "title": "Mean Field Variational Bayes estimation of fSAN",
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        "variational_fSAN"
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    },
    {
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      "title": "Perform variational inference using multiple starting points.",
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        "print.multistart",
        "variational_multistart"
      ]
    }
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