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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "MS2Quant" in publications use:'
type: software
title: 'MS2Quant: Ionization efficiency prediction and quantification of unidentified
  chemicals'
version: 1.1.0
doi: 10.32614/CRAN.package.MS2Quant
abstract: 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.
authors:
- family-names: Sepman
  given-names: Helen
  email: helen.sepman@aces.su.se
  orcid: https://orcid.org/0000-0002-8222-9962
repository: https://rickhelmus.r-universe.dev
commit: 46e0c77ef4961a29a73a388105eeb28e33839630
contact:
- family-names: Sepman
  given-names: Helen
  email: helen.sepman@aces.su.se
  orcid: https://orcid.org/0000-0002-8222-9962