Data sets from a variety of biological sample matrices, 
    analysed using a number of mass spectrometry based metabolomic analytical techniques.
    The example data sets are stored remotely using GitHub releases 
    <https://github.com/aberHRML/metaboData/releases> which can be accessed from R using the package.
    The package also includes the 'abr1' FIE-MS data set from the 'FIEmspro' package <https://users.aber.ac.uk/jhd/> <doi:10.1038/nprot.2007.511>.

A framework for deconvolution, alignment and postprocessing
    of 1-dimensional (1d) nuclear magnetic resonance (NMR) spectra, resulting in
    a data matrix of aligned signal integrals. The deconvolution part uses the
    algorithm described in Koh et al. (2009) <doi:10.1016/j.jmr.2009.09.003>.
    The alignment part is based on functions from the 'speaq' package, described
    in Beirnaert et al. (2018) <doi:10.1371/journal.pcbi.1006018> and Vu et al.
    (2011) <doi:10.1186/1471-2105-12-405>. A detailed description and evaluation
    of an early version of the package, 'MetaboDecon1D v0.2.2', can be found in
    Haeckl et al. (2021) <doi:10.3390/metabo11070452>.

Fits probabilistic principal components analysis,
        probabilistic principal components and covariates analysis and
        mixtures of probabilistic principal components models to
        metabolomic spectral data.

Dataset and functions from the meta-analysis published in Medicine & Science in Sports & Exercise. 
    It contains all the data and functions to reproduce the analysis.
    "Effectiveness of HIIE versus MICT in Improving Cardiometabolic Risk Factors in Health and Disease: A Meta-analysis".
    Felipe Mattioni Maturana, Peter Martus, Stephan Zipfel, Andreas M Nieß (2020) <doi:10.1249/MSS.0000000000002506>.

An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups. 
 Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary. 
 Several methods were implemented to reduce the metabolomics matrix such as the  principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9> , 
 the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, the 
 elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>. 
 Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. 
 Large scale cross validation can be performed in  order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.