Package: MS2Quant 1.1.0

Helen Sepman

MS2Quant: Ionization efficiency prediction and quantification of unidentified chemicals

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:Helen Sepman [aut, cre]

MS2Quant_1.1.0.tar.gz
MS2Quant_1.1.0.zip(r-4.5)MS2Quant_1.1.0.zip(r-4.4)MS2Quant_1.1.0.zip(r-4.3)
MS2Quant_1.1.0.tgz(r-4.4-any)MS2Quant_1.1.0.tgz(r-4.3-any)
MS2Quant_1.1.0.tar.gz(r-4.5-noble)MS2Quant_1.1.0.tar.gz(r-4.4-noble)
MS2Quant_1.1.0.tgz(r-4.4-emscripten)MS2Quant_1.1.0.tgz(r-4.3-emscripten)
MS2Quant.pdf |MS2Quant.html✨
MS2Quant/json (API)

# Install 'MS2Quant' in R:

install.packages('MS2Quant', repos = c('https://rickhelmus.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/drewszabo/ms2quant/issues

Uses libs:

openjdk– OpenJDK Java runtime, using Hotspot JIT

On CRAN:

2.00 score 5 scripts 15 exports 61 dependencies

Last updated 1 years agofrom:46e0c77ef4 (on main). Checks:OK: 1 WARNING: 6. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Nov 03 2024
R-4.5-win	WARNING	Nov 03 2024
R-4.5-linux	WARNING	Nov 03 2024
R-4.4-win	WARNING	Nov 03 2024
R-4.4-mac	WARNING	Nov 03 2024
R-4.3-win	WARNING	Nov 03 2024
R-4.3-mac	WARNING	Nov 03 2024