Metabolomics and Spectral Data Analysis and Mining

Provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, feature selection and pathway analysis. Case studies can be found on the website: <>. This package suggests 'rcytoscapejs', a package not in mainstream repositories. If you need to install it, use: devtools::install_github('cytoscape/[email protected]').


Reference manual

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3.1.6 by Miguel Rocha, 4 months ago

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Authors: Christopher Costa <[email protected]> [aut] , Marcelo Maraschin <[email protected]> [aut] , Miguel Rocha <[email protected]> [aut, cre] , Sara Cardoso <[email protected]> [aut] , Telma Afonso <[email protected]> [aut] , Bruno Pereira <[email protected]> [aut] , C. Beleites [cph] , Jie Hao [cph] ,

Documentation:   PDF Manual  

GPL (>= 2) license

Imports caret, e1071, ggplot2, impute, ellipse, GGally, pcaPP, compare, baseline, MASS, pls, readJDX, speaq, genefilter, RColorBrewer, grDevices, graphics, methods, stats, utils, Metrics, imputeTS, specmine.datasets, mrbin, plotly, narray

Suggests ggdendro, reticulate, qdap, qpdf, scatterplot3d, MAIT, xcms, KEGGgraph, KEGGREST, rcytoscapejs, rgl, grid, curl, RCurl, pins, knitr, rmarkdown

System requirements: Python (>=3.5.2) and the following python module: nmrglue.

See at CRAN