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  "Title": "Density Regression via Dirichlet Process Mixtures of Normal\nStructured Additive Regression Models",
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  "Date": "2025-01-30",
  "Authors@R": "c(\nperson(given = \"Maria Xose\", family = \"Rodriguez-Alvarez\",\nemail = \"mxrodriguez@uvigo.gal\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-1329-9238\")),\nperson(given = \"Vanda\", family = \"Inacio\", email = \"Vanda.Inacio@ed.ac.uk\", role = c(\"aut\"), comment = c(ORCID = \"0000-0001-8084-1616\")))",
  "Description": "Implements a flexible, versatile, and computationally\ntractable model for density regression based on a\nsingle-weights dependent Dirichlet process mixture of normal\ndistributions model for univariate continuous responses. The\nmodel assumes an additive structure for the mean of each\nmixture component and the effects of continuous covariates are\ncaptured through smooth nonlinear functions. The key components\nof our modelling approach are penalised B-splines and their\nbivariate tensor product extension. The proposed method can\nalso easily deal with parametric effects of categorical\ncovariates, linear effects of continuous covariates,\ninteractions between categorical and/or continuous covariates,\nvarying coefficient terms, and random effects. Please see\nRodriguez-Alvarez, Inacio et al. (2025) for more details.",
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  "Author": "Maria Xose Rodriguez-Alvarez [aut, cre]\n(<https://orcid.org/0000-0002-1329-9238>), Vanda Inacio [aut]\n(<https://orcid.org/0000-0001-8084-1616>)",
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