EMbC: Expectation-Maximization Binary Clustering

Unsupervised, multivariate, clustering algorithm yielding a meaningful binary clustering taking into account the uncertainty in the data. A specific constructor for trajectory movement analysis yields behavioural annotation of the tracks based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator.

Version: 1.9.3
Depends: move
Imports: sp, methods, RColorBrewer, mnormt, maptools
Suggests: rgl, knitr
Published: 2015-07-28
Author: Joan Garriga, John R.B. Palmer, Aitana Oltra, Frederic Bartumeus
Maintainer: Joan Garriga <jgarriga at ceab.csic.es>
License: GPL-3 | file LICENSE
URL: 'Expectation-Maximization Binary Clustering for Behavioural Annotation', (submitted to PLOS ONE)
NeedsCompilation: no
CRAN checks: EMbC results


Reference manual: EMbC.pdf
Vignettes: The EMbC R-package: quick reference
Package source: EMbC_1.9.3.tar.gz
Windows binaries: r-devel: EMbC_1.9.3.zip, r-release: EMbC_1.9.3.zip, r-oldrel: EMbC_1.9.3.zip
OS X Snow Leopard binaries: r-release: EMbC_1.9.3.tgz, r-oldrel: not available
OS X Mavericks binaries: r-release: EMbC_1.9.3.tgz