{ "_id": "6ab10b563d719c1f8408626c", "Package": "glmpermu", "Title": "Permutation-Based Inference for Generalized Linear Models", "Version": "0.0.1", "Authors@R": "c(person(\"Xuekui\",\"Zhang\",role=c(\"aut\",\"cre\"), email=\"xuekui@uvic.ca\"),\nperson(\"Li\",\"Xing\",role=\"aut\"),\nperson(\"Jing\",\"Zhang\",role=\"aut\"),\nperson(\"Soojeong\",\"Kim\",role=\"aut\"))", "Description": "In practical applications, the assumptions underlying\ngeneralized linear models frequently face violations, including\nincorrect specifications of the outcome variable's distribution\nor omitted predictors. These deviations can render the results\nof standard generalized linear models unreliable. As the sample\nsize increases, what might initially appear as minor issues can\nescalate to critical concerns. To address these challenges, we\nadopt a permutation-based inference method tailored for\ngeneralized linear models. This approach offers robust\nestimations that effectively counteract the mentioned problems,\nand its effectiveness remains consistent regardless of the\nsample size.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.1", "Maintainer": "Xuekui Zhang ", "NeedsCompilation": "no", "Packaged": { "Date": "2026-09-21 10:45:20 UTC", "User": "root" }, "Author": "Xuekui Zhang [aut, cre], Li Xing [aut], Jing Zhang [aut],\nSoojeong Kim [aut]", "Repository": "https://ubcxzhang.r-universe.dev", "Date/Publication": "2024-03-13 02:33:43 UTC", "RemoteUrl": "https://github.com/cran/glmpermu", "RemoteRef": "HEAD", "RemoteSha": "a6490814ae2ffc14b012dc28bb412b9619402335", "_user": "ubcxzhang", "_type": "src", "_file": "glmpermu_0.0.1.tar.gz", "_fileid": "https://r2.ropensci.org/4710f52fff10c776db1ce9d7b6575998ee1cc071e757320d6e4c41a83beed788", "_filesize": 99333, "_sha256": "4710f52fff10c776db1ce9d7b6575998ee1cc071e757320d6e4c41a83beed788", "_expires": "2026-12-30T10:47:49.000Z", 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