{ "_id": "6ad74e514b18b90089999b9a", "Package": "AddiVortes", "Title": "(Bayesian) Additive Voronoi Tessellations", "Version": "1.0.1", "Authors@R": "c(person(\"Adam\", \"Stone\", , \"adam.stone2@durham.ac.uk\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0009-0004-0058-6117\")),\nperson(\"John Paul\", \"Gosling\", , \"john-paul.gosling@durham.ac.uk\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-4072-3022\")),\nperson(\"Andrew\", \"Iskauskas\", ,\"andrew.iskauskas@durham.ac.uk\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0000-0003-2825-3651\")),\nperson(\"Leo\", \"Thomson\", , \"leo@feasibly.co.uk\",\nrole = c(\"aut\")))", "Description": "Implements the Bayesian Additive Voronoi Tessellation\nmodel for non-parametric regression, classification and machine\nlearning as introduced in Stone and Gosling (2025)\n. This package provides a\nflexible alternative to BART (Bayesian Additive Regression\nTrees) using Voronoi tessellations instead of trees. Users can\nfit Bayesian regression and probit classification models,\nestimate the associated posterior distributions and make\npredictions. 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