{ "_id": "6a1f1f93b401979e734201a8", "Package": "iimi", "Title": "Identifying Infection with Machine Intelligence", "Version": "1.2.2", "Authors@R": "c(\nperson(\"Haochen\", \"Ning\", , \"hning@uvic.ca\", role = c(\"aut\")),\nperson(\"Ian\", \"Boyes\", , \"ian.boyes@inspection.gc.ca\", role = c(\"aut\")),\nperson(\"Ibrahim\", \"Numanagić\", , \"inumanag@uvic.ca\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-2970-7937\")),\nperson(\"Michael\", \"Rott\", , \"mike.rott@inspection.gc.ca\", role = c(\"aut\")),\nperson(\"Li\", \"Xing\", , \"lix491@math.usask.ca\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-4186-7909\")),\nperson(\"Xuekui\", \"Zhang\", , \"xuekui@uvic.ca\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-4728-2343\")))", "Description": "A novel machine learning method for plant viruses\ndiagnostic using genome sequencing data. This package includes\nthree different machine learning models, random forest,\nXGBoost, and elastic net, to train and predict mapped genome\nsamples. Mappability profile and unreliable regions are\nintroduced to the algorithm, and users can build a mappability\nprofile from scratch with functions included in the package.\nPlotting mapped sample coverage information is provided.", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "LazyData": "true", "VignetteBuilder": "knitr", "RdMacros": "Rdpack", "License": "MIT + file LICENSE", "LazyDataCompression": "xz", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-16 08:03:19 UTC", "User": "root" }, "Author": "Haochen Ning [aut], Ian Boyes [aut], Ibrahim Numanagić [aut]\n(ORCID: ), Michael Rott\n[aut], Li Xing [aut] (ORCID:\n), Xuekui Zhang [aut,\ncre] (ORCID: )", "Maintainer": "Xuekui Zhang ", "Config/pak/sysreqs": "make libbz2-dev libicu-dev liblzma-dev xz-utils\nzlib1g-dev", "Repository": "https://ubcxzhang.r-universe.dev", "Date/Publication": "2025-12-04 06:50:17 UTC", "RemoteUrl": "https://github.com/cran/iimi", "RemoteRef": "HEAD", "RemoteSha": 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