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  "Title": "A Laboratory for Recursive Partytioning",
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  "Authors@R": "c(person(\"Torsten\", \"Hothorn\", role = c(\"aut\", \"cre\"),\nemail = \"Torsten.Hothorn@R-project.org\",\ncomment = c(ORCID = \"0000-0001-8301-0471\")),\nperson(\"Kurt\", \"Hornik\", role = \"aut\",\nemail = \"Kurt.Hornik@R-project.org\",\ncomment = c(ORCID = \"0000-0003-4198-9911\")),\nperson(\"Carolin\", \"Strobl\", role = \"aut\",\nemail = \"carolin.strobl@psychologie.uzh.ch\",\ncomment = c(ORCID = \"0000-0003-0952-3230\")),\nperson(\"Achim\", \"Zeileis\", role = \"aut\",\nemail = \"Achim.Zeileis@R-project.org\",\ncomment = c(ORCID = \"0000-0003-0918-3766\")))",
  "Description": "A computational toolbox for recursive partitioning. The\ncore of the package is ctree(), an implementation of\nconditional inference trees which embed tree-structured\nregression models into a well defined theory of conditional\ninference procedures. This non-parametric class of regression\ntrees is applicable to all kinds of regression problems,\nincluding nominal, ordinal, numeric, censored as well as\nmultivariate response variables and arbitrary measurement\nscales of the covariates. Based on conditional inference trees,\ncforest() provides an implementation of Breiman's random\nforests. The function mob() implements an algorithm for\nrecursive partitioning based on parametric models (e.g. linear\nmodels, GLMs or survival regression) employing parameter\ninstability tests for split selection. Extensible functionality\nfor visualizing tree-structured regression models is available.\nThe methods are described in Hothorn et al. (2006)\n<doi:10.1198/106186006X133933>, Zeileis et al. (2008)\n<doi:10.1198/106186008X319331> and Strobl et al. (2007)\n<doi:10.1186/1471-2105-8-25>.",
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