{ "_id": "6a1f08c5b401979e7341c1cb", "Package": "AirScreen", "Type": "Package", "Title": "Feature Screening via Adaptive Iterative Ridge (Air-HOLP and\nAir-OLS)", "Version": "0.1.0", "Authors@R": "c(\nperson(\ngiven = \"Ibrahim\", family = \"Joudah\",\nemail = \"ibrahim.joudah@mq.edu.au\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0397-6266\")\n),\nperson(\ngiven = \"Samuel\", family = \"Muller\",\nemail = \"samuel.muller@mq.edu.au\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-3087-8127\")\n),\nperson(\ngiven = \"Houying\", family = \"Zhu\",\nemail = \"houying.zhu@mq.edu.au\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-2515-7413\")\n)\n)", "Description": "Implements two complementary high-dimensional feature\nscreening methods, Adaptive Iterative Ridge High-dimensional\nOrdinary Least-squares Projection (Air-HOLP, suitable when the\nnumber of predictors p is greater than or equal to the sample\nsize n) and Adaptive Iterative Ridge Ordinary Least Squares\n(Air-OLS, for n greater than p). Also provides helper functions\nto generate compound-symmetry and AR(1) correlated data, plus a\nunified Air() front end and a summary method. For\nmethodological details see Joudah, Muller and Zhu (2025)\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "URL": "https://github.com/Logic314/Air-HOLP", "Config/testthat/edition": "3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-18 06:19:05 UTC", "User": "root" }, "Author": "Ibrahim Joudah [aut, cre] (ORCID:\n), Samuel Muller [aut]\n(ORCID: ), Houying Zhu\n[aut] (ORCID: )", "Maintainer": "Ibrahim Joudah ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-07-31 10:45:04 UTC", "RemoteUrl": "https://github.com/cran/AirScreen", "RemoteRef": "HEAD", "RemoteSha": "c578d7ef4845300b8f6a387e95c828825a886747", "MD5sum": "cae91f28ae6ba07b0fd0d9b37b55d9de", "_user": "cran", "_type": "src", "_file": "AirScreen_0.1.0.tar.gz", "_fileid": "56f522550219b7b5c64a75897599fae0420c18a131a0372b6ad9f1f100c02129", "_filesize": 167561, "_sha256": "56f522550219b7b5c64a75897599fae0420c18a131a0372b6ad9f1f100c02129", "_created": 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