{ "_id": "6a16951aacfb0bcc41d7abca", "Package": "MS2Quant", "Title": "Ionization efficiency prediction and quantification of\nunidentified chemicals", "Version": "1.1.0", "Authors@R": "person(\"Helen\", \"Sepman\", , \"helen.sepman@aces.su.se\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-8222-9962\"))", "Description": "MS2Quant harvests pre-trained xgbTree algorithm-based\nionization efficiency prediction model. Using strucutral\ninformation (SMILES) or predicted fingerprints calculated with\nSIRIUS+CSI_FingerID software, ionization efficiency can be\npredicted. If calibrants have been measured together with\nsuspects subject to quantification, predicted ionization\nefficiencies can be converted into measurement-specific\nresponse factors and concentration of unknown chemcals can be\nestimated.", "License": "`use_mit_license()`, `use_gpl3_license()` or friends to pick a\nlicense", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.1", "Config/pak/sysreqs": "make default-jdk libicu-dev libpng-dev libxml2-dev\nlibx11-dev", "Repository": "https://rickhelmus.r-universe.dev", "Date/Publication": "2026-02-26 08:45:42 UTC", "RemoteUrl": "https://github.com/kruvelab/MS2Quant", "RemoteRef": "main", "RemoteSha": "d01f631a9c434a1eb2548ac53e38374f905355b8", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-27 06:49:47 UTC", "User": "root" }, "Author": "Helen Sepman [aut, cre] (ORCID:\n)", "Maintainer": "Helen Sepman ", "MD5sum": "abcf6c09efd48e9d6b5802accd356268", "_user": "rickhelmus", "_type": "src", "_file": 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