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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-4.7)MS2Quant_1.1.0.zip(r-4.6)MS2Quant_1.1.0.zip(r-4.5)
MS2Quant_1.1.0.tgz(r-4.6-any)MS2Quant_1.1.0.tgz(r-4.5-any)
MS2Quant_1.1.0.tar.gz(r-4.7-any)MS2Quant_1.1.0.tar.gz(r-4.6-any)
MS2Quant_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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/kruvelab/ms2quant/issues

Uses libs:

openjdk– OpenJDK Java runtime, using Hotspot JIT

On CRAN:

openjdk

3.18 score 5 stars 6 scripts 15 exports 95 dependencies

Last updated from:d01f631a9c (on main). Checks:7 WARNING, 2 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	WARNING	197
source / vignettes	OK	212
linux-release-x86_64	WARNING	208
macos-release-arm64	WARNING	121
macos-oldrel-arm64	WARNING	146
windows-devel	WARNING	146
windows-release	WARNING	146
windows-oldrel	WARNING	144
wasm-release	OK	148