Package: MS2Quant
Title: Ionization efficiency prediction and quantification of
        unidentified chemicals
Version: 1.1.0
Authors@R: 
    person("Helen", "Sepman", , "helen.sepman@aces.su.se", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-8222-9962"))
Description: MS2Quant harvests pre-trained xgbTree algorithm-based
        ionization efficiency prediction model. Using strucutral
        information (SMILES) or predicted fingerprints calculated with
        SIRIUS+CSI_FingerID software, ionization efficiency can be
        predicted. If calibrants have been measured together with
        suspects subject to quantification, predicted ionization
        efficiencies can be converted into measurement-specific
        response factors and concentration of unknown chemcals can be
        estimated.
License: `use_mit_license()`, `use_gpl3_license()` or friends to pick a
        license
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.1
Imports: caret, dplyr, enviPat, ggplot2, rcdk, rcdklibs, readr, rJava,
        rlist, stringr, tibble, tidyr, xfun, xgboost
Config/pak/sysreqs: make default-jdk libicu-dev libpng-dev libxml2-dev
        libx11-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: 2026-05-27 06:49:47 UTC; root
Author: Helen Sepman [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-8222-9962>)
Maintainer: Helen Sepman <helen.sepman@aces.su.se>
Depends: R (>= 3.5.0)