{ "_id": "6a102026acfb0bcc41c8b765", "Package": "dirichletprocess", "Type": "Package", "Title": "Build Dirichlet Process Objects for Bayesian Modelling", "Version": "0.4.2", "Authors@R": "c(\nperson(\"Gordon\", \"J. Ross\", email=\"gordon@gordonjross.co.uk\", role=c(\"aut\")),\nperson(\"Dean\", \"Markwick\", email=\"dean.markwick@talk21.com\", role=c(\"aut\", \"cre\")),\nperson(\"Kees\", \"Mulder\", email=\"keestimmulder@gmail.com\", role=c(\"ctb\"),\ncomment = c(ORCID = \"0000-0002-5387-3812\")),\nperson(\"Giovanni\", \"Sighinolfi\", email =\"giovanni.sighinolfi2@studio.unibo.it\", role=c(\"ctb\")),\nperson(\"Filippo Fiocchi\", email=\"filippofiocchi1@gmail.com\", role=c(\"ctb\"))\n)", "Maintainer": "Dean Markwick ", "Description": "Perform nonparametric Bayesian analysis using Dirichlet\nprocesses without the need to program the inference algorithms.\nUtilise included pre-built models or specify custom models and\nallow the 'dirichletprocess' package to handle the Markov chain\nMonte Carlo sampling. Our Dirichlet process objects can act as\nbuilding blocks for a variety of statistical models including\nand not limited to: density estimation, clustering and prior\ndistributions in hierarchical models. See Teh, Y. W. (2011)\n,\namong many other sources.", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "URL": "https://github.com/dm13450/dirichletprocess,\nhttps://dm13450.github.io/dirichletprocess/", "BugReports": "https://github.com/dm13450/dirichletprocess/issues", "RoxygenNote": "7.2.3", "Repository": "https://dm13450.r-universe.dev", "Date/Publication": "2023-08-25 07:14:14 UTC", "RemoteUrl": "https://github.com/dm13450/dirichletprocess", "RemoteRef": "HEAD", "RemoteSha": "e161437e3d115af266b251494fecb385d24ac4a7", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-14 06:24:07 UTC", "User": "root" }, "Author": "Gordon J. 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