{ "_id": "6a51dcaa9b92047c28d51d48", "Package": "evtree", "Title": "Evolutionary Learning of Globally Optimal Trees", "Version": "1.0-8", "Date": "2019-05-19", "Authors@R": "c(person(given = \"Thomas\", family = \"Grubinger\", role = c(\"aut\", \"cre\"), email = \"ThomasGrubinger@gmail.com\"),\nperson(given = \"Achim\", family = \"Zeileis\", role = \"aut\", email = \"Achim.Zeileis@R-project.org\",\ncomment = c(ORCID = \"0000-0003-0918-3766\")),\nperson(given = \"Karl-Peter\", family = \"Pfeiffer\", role = \"aut\", email = \"Karl-Peter.Pfeiffer@i-med.ac.at\"))", "Description": "Commonly used classification and regression tree methods\nlike the CART algorithm are recursive partitioning methods that\nbuild the model in a forward stepwise search. Although this\napproach is known to be an efficient heuristic, the results of\nrecursive tree methods are only locally optimal, as splits are\nchosen to maximize homogeneity at the next step only. An\nalternative way to search over the parameter space of trees is\nto use global optimization methods like evolutionary\nalgorithms. The 'evtree' package implements an evolutionary\nalgorithm for learning globally optimal classification and\nregression trees in R. CPU and memory-intensive tasks are fully\ncomputed in C++ while the 'partykit' package is leveraged to\nrepresent the resulting trees in R, providing unified\ninfrastructure for summaries, visualizations, and predictions.", "LazyData": "yes", "License": "GPL-2 | GPL-3", "Repository": "https://zeileis.r-universe.dev", "Date/Publication": "2026-03-13 19:02:53 UTC", "RemoteUrl": "https://github.com/r-forge/partykit", "RemoteRef": "HEAD", "RemoteSha": "861432cfc097e26ea36940c174227ff1151eac6e", "RemoteSubdir": "pkg/evtree", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-07-11 05:57:16 UTC", "User": "root" }, "Author": "Thomas Grubinger [aut, cre],\nAchim Zeileis [aut] (ORCID: ),\nKarl-Peter Pfeiffer [aut]", "Maintainer": "Thomas Grubinger ", "_user": "zeileis", "_type": "src", "_file": "evtree_1.0-8.tar.gz", "_fileid": "https://r2.ropensci.org/101c9e514f51d6670dbf3a33f768104a3e8cdef659e37ab2f554ae4419584848", "_filesize": 1704813, "_sha256": 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