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  "Title": "Variational Approximations for Generalized Additive Models",
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  "Date": "2019-12-07",
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  "Authors@R": "c(\nperson(\"Han Lin\", \"Shang\", email=\"hanlin.shang@anu.edu.au\", role=c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0003-1769-6430\")),\nperson(\"Francis K.C.\", \"Hui\", role=\"aut\", comment = c(ORCID = \"0000-0003-0765-3533\"))\n)",
  "Maintainer": "Han Lin Shang <hanlin.shang@anu.edu.au>",
  "Description": "Fits generalized additive models (GAMs) using a\nvariational approximations (VA) framework. In brief, the VA\nframework provides a fully or at least closed to fully\ntractable lower bound approximation to the marginal likelihood\nof a GAM when it is parameterized as a mixed model (using\npenalized splines, say). In doing so, the VA framework aims\noffers both the stability and natural inference tools available\nin the mixed model approach to GAMs, while achieving\ncomputation times comparable to that of using the penalized\nlikelihood approach to GAMs. See Hui et al. (2018)\n<doi:10.1080/01621459.2018.1518235>.",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-03 07:00:01 UTC",
    "User": "root"
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  "Author": "Han Lin Shang [aut, cre, cph]\n(<https://orcid.org/0000-0003-1769-6430>), Francis K.C. Hui\n[aut] (<https://orcid.org/0000-0003-0765-3533>)",
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      "title": "Variational approximations for generalized additive models",
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