Summary: KODAMA, a novel learning algorithm for unsupervised feature extraction, is specifically designed for analysing noisy and high-dimensional datasets. Here we present an R package of the algorithm with additional functions that allow improved interpretation of high-dimensional data. The package requires no additional software and runs on all major platforms. Availability and Implementation: KODAMA is freely available from the R archive CRAN ( http://cran.r-project.org ). The software is distributed under the GNU General Public License (version 3 or later). Contact: s.cacciatore@imperial.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.
