Description: Functions to classify mass spectra in known categories, and to determine discriminant mass-over-charge values. It includes easy-to-use functions for pre-processing mass spectra, functions to determine discriminant mass-over-charge values (m/z) from a library of mass spectra corresponding to different categories, and functions to predict the category (species, phenotypes, etc.) associated to a mass spectrum from a list of selected mass-over-charge values. Three vignettes illustrating how to use the functions of this package from real data sets are also available online to help users: <https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_Ecrobiav3.html>, <https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_Klebsiellav3.html> and <https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_DAv3.html>.
Depends: R (>= 4.0), cp4p, caret, statmod,
reshape2, ggplot2, nnet, dplyr, fuzzyjoin, VSURF, metap,
xgboost, glmnet, performanceEstimation, mltools, mclust, UBL,

Authors@R: c(person("Cristina", "Tortora", role = c("aut", "cre","cph"), email = "grikris1@gmail.com", comment = c(ORCID = "0000-0001-8351-3730")),
person("Louis", "Tran", role = c("aut")))
Description: Model based clustering using
the multivariate multiple Scaled t (MST) and multivariate multiple
scaled contaminated normal (MSCN) distributions. The MST is an
extension of the multivariate Student-t distribution to include
flexible tail behaviors, Forbes, F. & Wraith, D. (2014) <doi:10.1007/s11222-013-9414-4>. The MSCN represents a  heavy-tailed
generalization of the multivariate normal (MN) distribution to
model elliptical contoured scatters in the presence of mild outliers
(also referred to as "bad" points) and automatically detect bad points, Punzo, A. & Tortora, C. (2021) <doi:10.1177/1471082X19890935>.
Imports: gtools, Matrix, mclust, mnormt, mvtnorm, psych, cluster,
Author: Cristina Tortora [aut, cre, cph]

Title: Combine Data from Positive and Negative Ionization Mode Finding
Description: Find common entities detected in both positive and negative
ionization mode, delete this entity in the less sensible mode and combine both