Package: segmenTier
Type: Package
Title: Similarity-Based Segmentation of Multidimensional Signals
Version: 0.1.2
Author: Rainer Machne, Douglas B. Murray, Peter F. Stadler
URL: https://github.com/raim/segmenTier
BugReports: https://github.com/raim/segmenTier/issues
Maintainer: Rainer Machne <raim@tbi.univie.ac.at>
Description: 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.
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.7)
Suggests: flowMerge, flowClust, flowCore, knitr, rmarkdown
LinkingTo: Rcpp
Encoding: UTF-8
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-02-09 13:04:24 UTC; raim
Repository: CRAN
Date/Publication: 2019-02-18 23:00:03 UTC
Built: R 3.6.3; x86_64-w64-mingw32; 2020-08-05 01:56:40 UTC; windows
Archs: i386, x64
