EMbC: Expectation-Maximization Binary Clustering

Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation").

Version: 2.0.2
Imports: Rcpp (≥ 0.11.0), sp, methods, RColorBrewer, mnormt, maptools
LinkingTo: Rcpp, RcppArmadillo
Suggests: move, rgl, knitr
Published: 2019-09-04
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: <doi:10.1371/journal.pone.0151984>
NeedsCompilation: yes
Materials: NEWS
In views: SpatioTemporal
CRAN checks: EMbC results

Downloads:

Reference manual: EMbC.pdf
Vignettes: The EMbC R-package: quick reference
Package source: EMbC_2.0.2.tar.gz
Windows binaries: r-devel: EMbC_2.0.2.zip, r-release: EMbC_2.0.2.zip, r-oldrel: EMbC_2.0.2.zip
OS X binaries: r-release: EMbC_2.0.2.tgz, r-oldrel: EMbC_2.0.2.tgz
Old sources: EMbC archive

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