Fitting regression models where, in addition to possible linear terms, one or more covariates have segmented (i.e., broken-line 
  or piece-wise linear) or stepmented (i.e. piece-wise constant) effects. 
  Multiple breakpoints for the same variable are allowed. The estimation method is discussed in Muggeo (2003, <doi:10.1002/sim.1545>) and 
  illustrated in Muggeo (2008, <https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf>). An approach for hypothesis testing is presented 
  in Muggeo (2016, <doi:10.1080/00949655.2016.1149855>), and interval estimation for the breakpoint is discussed in Muggeo (2017, <doi:10.1111/anzs.12200>). 
  Segmented mixed models, i.e. random effects in the change point, are discussed in Muggeo (2014, <doi:10.1177/1471082X13504721>).
  Estimation of piecewise-constant relationships and changepoints (mean-shift models) is 
  discussed in Fasola et al. (2018, <doi:10.1007/s00180-017-0740-4>).

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.

Given a likelihood provided by the user, this package applies it
    to a given matrix dataset in order to find change points in the data that
    maximize the sum of the likelihoods of all the segments. This package provides
    a handful of algorithms with different time complexities and assumption compromises
    so the user is able to choose the best one for the problem at hand. The implementation
    of the segmentation algorithms in this package are based on the paper by Bruno M. de Castro,
	Florencia Leonardi (2018) <arXiv:1501.01756>. The Berlin
	weather sample dataset was provided by Deutscher Wetterdienst <https://dwd.de/>.
	You can find all the references in the Acknowledgments section of this package's
	repository via the URL below.