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 classbased templates provided by the struct (Statistics in R Using Classbased Templates) package. The toolbox includes preprocessing 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 crossvalidation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs.
Package details 


Author  Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] 
Bioconductor views  Metabolomics WorkflowStep 
Maintainer  Gavin Rhys Lloyd <g.r.lloyd@bham.ac.uk> 
License  GPL3 
Version  1.2.0 
Package repository  View on Bioconductor 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.