Similarity-Based Segmentation of Multidimensional Signals R package segmenTier version 0.1.2 A dynamic programming solution to segmentation based on maximization of arbitrary similarity measures within segments. The general idea, theory and this implementation are described in Machne, Murray & Stadler (2017) <doi:10.1038/s41598-017-12401-8>. In addition to the core algorithm, the package provides time-series processing and clustering functions as described in the publication. These are generally applicable where a `k-means` clustering yields meaningful results, and have been specifically developed for clustering of the Discrete Fourier Transform of periodic gene expression data (`circadian' or `yeast metabolic oscillations'). This clustering approach is outlined in the supplemental material of Machne & Murray (2012) <doi:10.1371/journal.pone.0037906>), and here is used as a basis of segment similarity measures. Notably, the time-series processing and clustering functions can also be used as stand-alone tools, independent of segmentation, e.g., for transcriptome data already mapped to genes. Software Imports: Rcpp (>= 0.12.7) LinkingTo: Rcpp Suggests: flowMerge, flowClust, flowCore, knitr, rmarkdown Rainer Machne <raim@tbi.univie.ac.at> Comprehensive R Archive Network (CRAN) Rainer Machne, Douglas B. Murray, Peter F. Stadler GPL (>= 2) 2019-02-18 application/tgz https://CRAN.R-project.org/package=segmenTier doi:10.32614/CRAN.package.segmenTier