{ "_id": "6a30597c3efcd9bda4343390", "Package": "sanba", "Type": "Package", "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 ", "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, D’Angelo, Canale, Yu,\nGuindani (2023) , D’Angelo, Denti\n(2026) .", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Language": "en-US", "Config/roxygen2/version": "8.0.0", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-15 19:53:07 UTC", "User": "root" }, "Author": "Francesco Denti [aut, cre, cph] (ORCID:\n), Laura D'Angelo [aut]\n(ORCID: )", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-06-15 19:10:57 UTC", "RemoteUrl": "https://github.com/cran/sanba", "RemoteRef": "HEAD", "RemoteSha": "4cc88e2ef620ff8ec64131bd32a4a140be8f6482", "MD5sum": "8223317f4f39dbb2b1236dedff57752a", "_user": "cran", "_type": "src", "_file": "sanba_0.0.4.tar.gz", "_fileid": "5f92b4790d4ba4eb534ce29f8427999ff17fdc270038dcca43e597b00a27c68a", "_filesize": 452108, "_sha256": "5f92b4790d4ba4eb534ce29f8427999ff17fdc270038dcca43e597b00a27c68a", "_created": "2026-06-15T19:53:07.000Z", "_published": 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