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An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). Ontology terms have been integrated to provide standardised definitions for the different methods, inputs and outputs.

peakCombiner, a fully R based, user-friendly, transparent, and customizable tool that allows even novice R users to create a high-quality consensus peak list. The modularity of its functions allows an easy way to optimize input and output data. A broad range of accepted input data formats can be used to create a consensus peak set that can be exported to a file or used as the starting point for most downstream peak analyses.