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  "Title": "Bayesian Generalized Additive Model Selection",
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  "Description": "Generalized additive model selection via approximate\nBayesian inference is provided. Bayesian mixed model-based\npenalized splines with spike-and-slab-type coefficient prior\ndistributions are used to facilitate fitting and selection. The\napproximate Bayesian inference engine options are: (1) Markov\nchain Monte Carlo and (2) mean field variational Bayes. Markov\nchain Monte Carlo has better Bayesian inferential accuracy, but\nrequires a longer run-time. Mean field variational Bayes is\nfaster, but less accurate. The methodology is described in He\nand Wand (2024) <doi:10.1007/s10182-023-00490-y>.",
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  "Author": "Virginia X. He [aut] (ORCID:\n<https://orcid.org/0000-0002-0238-5018>), Matt P. Wand [aut,\ncre] (ORCID: <https://orcid.org/0000-0003-2555-896X>)",
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