{
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  "Package": "sanba",
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  "Title": "Fitting Shared Atoms Nested Models via MCMC or Variational Bayes",
  "Version": "0.0.4",
  "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/sanba",
  "BugReports": "https://github.com/fradenti/sanba/issues",
  "Description": "An efficient tool for fitting nested mixture models based\non a shared set of atoms via Markov Chain Monte Carlo and\nvariational inference algorithms. Specifically, the package\nimplements the common atoms model (Denti et al., 2023), its\nfinite version (similar to D'Angelo et al., 2023), and a hybrid\nfinite-infinite model (D'Angelo and Denti, 2026). All models\nimplement univariate nested mixtures with Gaussian kernels\nequipped with a normal-inverse gamma prior distribution on the\nparameters. Additional functions are provided to help analyze\nthe results of the fitting procedure. References: Denti,\nCamerlenghi, Guindani, Mira (2023)\n<doi:10.1080/01621459.2021.1933499>, D’Angelo, Canale, Yu,\nGuindani (2023) <doi:10.1111/biom.13626>, D’Angelo, Denti\n(2026) <doi:10.1214/24-BA1458>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Language": "en-US",
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  "Repository": "https://fradenti.r-universe.dev",
  "Date/Publication": "2026-06-08 14:23:24 UTC",
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    "estimate_partition",
    "fit_CAM",
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    "get_sim",
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    "number_clusters",
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      "title": "Compute Posterior Predictive Density Samples for a Group",
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        "compute_postdens"
      ]
    },
    {
      "page": "compute_psm",
      "title": "Compute and Plot the Posterior Similarity Matrix",
      "topics": [
        "compute_psm"
      ]
    },
    {
      "page": "estimate_G",
      "title": "Estimate the Atoms and Weights of the Discrete Mixing Distributions",
      "topics": [
        "estimate_G",
        "plot.SANvi_G",
        "print.SANvi_G",
        "summary.SANvi_G"
      ]
    },
    {
      "page": "estimate_partition",
      "title": "Estimate the Observational and Distributional Partition",
      "topics": [
        "estimate_partition",
        "estimate_partition.SANmcmc",
        "estimate_partition.SANvi",
        "plot.partition_mcmc",
        "plot.partition_vi",
        "print.partition_mcmc",
        "print.partition_vi",
        "summary.partition_mcmc",
        "summary.partition_vi"
      ]
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      "title": "Fit the Common Atoms Mixture Model",
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        "fit_CAM"
      ]
    },
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      "page": "fit_fiSAN",
      "title": "Fit the Finite-Infinite Shared Atoms Mixture Model",
      "topics": [
        "fit_fiSAN"
      ]
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      "page": "fit_fSAN",
      "title": "Fit the Finite Shared Atoms Mixture Model",
      "topics": [
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      ]
    },
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      "title": "Accessor Functions for SAN Model Outputs",
      "topics": [
        "get_model",
        "get_model.SANmcmc",
        "get_model.SANvi",
        "get_params",
        "get_params.SANmcmc",
        "get_params.SANvi",
        "get_seed_best_run",
        "get_seed_best_run.SANvi",
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        "get_time",
        "get_time.SANmcmc",
        "get_time.SANvi"
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    },
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      "topics": [
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      ]
    },
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      "title": "Plot Variational Cluster Allocation Probabilities",
      "topics": [
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    },
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      "topics": [
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