{ "_id": "6a14237facfb0bcc41d3f0a9", "Package": "LongituRF", "Title": "Random Forests for Longitudinal Data", "Version": "0.9", "Authors@R": "person(given = \"Louis\",\nfamily = \"Capitaine\",\nrole = c(\"aut\", \"cre\"),\nemail = \"Louis.capitaine@u-bordeaux.fr\",comment = c(ORCID = \"0000-0001-6800-2342\"))", "Description": "Random forests are a statistical learning method widely\nused in many areas of scientific research essentially for its\nability to learn complex relationships between input and output\nvariables and also its capacity to handle high-dimensional\ndata. However, current random forests approaches are not\nflexible enough to handle longitudinal data. In this package,\nwe propose a general approach of random forests for\nhigh-dimensional longitudinal data. It includes a flexible\nstochastic model which allows the covariance structure to vary\nover time. Furthermore, we introduce a new method which takes\nintra-individual covariance into consideration to build random\nforests. The method is fully detailled in Capitaine et.al.\n(2020) Random forests for\nhigh-dimensional longitudinal data.", "License": "GPL-2", "Encoding": "UTF-8", "RoxygenNote": "7.1.1", "Repository": "https://sistm.r-universe.dev", "Date/Publication": "2022-05-15 20:03:01 UTC", "RemoteUrl": "https://github.com/sistm/longiturf", "RemoteRef": "HEAD", "RemoteSha": "3ac37353f9e0bfc9eba4ece3bc2f3610f326fc20", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-25 10:22:26 UTC", "User": "root" }, "Author": "Louis Capitaine [aut, cre] (ORCID:\n)", "Maintainer": "Louis Capitaine ", "MD5sum": "17999deb66dff4c798a33a5b9d692845", "_user": "sistm", "_type": "src", "_file": "LongituRF_0.9.tar.gz", "_fileid": "fc3c4bf47bee5faaab0f7769c90db6fc65921181bd9cf428dc1e6a9e76c544c1", "_filesize": 164035, "_sha256": "fc3c4bf47bee5faaab0f7769c90db6fc65921181bd9cf428dc1e6a9e76c544c1", "_created": "2026-05-25T10:22:26.000Z", "_published": "2026-05-25T10:25:03.134Z", "_distro": "noble", "_jobs": [ { "job": 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