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  "Title": "Fitting Shared Atoms Nested Models via Variational Bayes",
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  "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)",
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  "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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