Multivariate Analysis of Metabolomics Data using Random Forests

A collection of tools for multivariate analysis of metabolomics data, which includes several preprocessing methods (normalization, scaling) and various exploration and data visualization techniques (Principal Components Analysis and Multi Dimensional Scaling). The core of the package is the Random Forest algorithm used for the construction, optimization and validation of classification models with the aim of identifying potentially relevant biomarkers.


Reference manual

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1.0.1 by Piergiorgio Palla, 2 years ago

Browse source code at

Authors: Piergiorgio Palla, Giuliano Armano

Documentation:   PDF Manual  

GPL-3 license

Imports randomForest, ggplot2, UsingR, WilcoxCV, ROCR, methods

Depends on AUCRF

See at CRAN