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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", "Author": "Thomas Grubinger [aut, cre], Achim Zeileis [aut]\n(), Karl-Peter Pfeiffer\n[aut]", "Maintainer": "Thomas Grubinger ", "Repository/R-Forge/Project": "partykit", "Repository/R-Forge/Revision": "2594", "Repository/R-Forge/DateTimeStamp": "2019-05-26 18:21:12", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-08 08:53:42 UTC", "User": "root" }, "Repository": "https://thomasgrubinger-svg.r-universe.dev", "Date/Publication": "2019-05-26 18:40:04 UTC", "RemoteUrl": "https://github.com/cran/evtree", "RemoteRef": "HEAD", "RemoteSha": "e0d856a2a20e9e3f516b48a69b96719a226f5941", "MD5sum": "5328449a8680f7cfb8f6a5b96f6af3fe", "_user": "thomasgrubinger-svg", "_type": "src", "_file": "evtree_1.0-8.tar.gz", 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