Description
Bivariate Segmentation/Clustering Methods and Tools.
Description
Provides two methods for segmentation and joint segmentation/clustering of bivariate time-series. Originally intended for ecological segmentation (home-range and behavioural modes) but easily applied on other series, the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'. The segmentation method is a bivariate extension of Lavielle's method available in 'adehabitatLT' (Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>). This method rely on dynamic programming for efficient segmentation. The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm. This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method (formerly available in 'cghseg' package) to the bivariate case. The method is fully described in Patin et al (2018) <doi:10.1101/444794>.
README.md
segclust2d: bivariate segmentation with optional clustering for R
Introduction
segclust2d provides R code for a segmentation method that can be used on all bivariate time-series. The segmentation method can additionally be associated with a clustering algorithm. It was originally intended for ecological segmentation (home-range and behavioural modes) but can be easily applied on other type of time-series. The package also provides tools for analysing outputs from R packages moveHMM and marcher.
Website
Full documentation for segclust2d is available on this website: https://rpatin.github.io/segclust2d/
Three topics are discussed there, and are also available as vignettes in the R package:
- First, preparation of data to be analyzed with segmentation/clustering algorithm, including covariate calculations, subsampling and guidelines to prepare movement data.
- Second, a guide to run the segmentation and segmentation/clustering method, including advise on setting parameters.
- Finally, an overview of the possible outputs that can be generated with the package.
Installation
For the version :
install.packages("segclust2d")
If you want the newest , you can install segclust2d from github with:
devtools::install_github("rpatin/segclust2d")