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:	2.0.3
Imports:	Rcpp (≥ 0.11.0), sp, methods, RColorBrewer, mnormt, maptools
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	move, rgl, knitr
Published:	2019-12-16
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, Tracking
CRAN checks:	EMbC results

Documentation:

Reference manual:	EMbC.pdf
Vignettes:	The EMbC R-package: quick reference

Downloads:

Package source:	EMbC_2.0.3.tar.gz
Windows binaries:	r-devel: EMbC_2.0.3.zip, r-release: EMbC_2.0.3.zip, r-oldrel: EMbC_2.0.3.zip
macOS binaries:	r-release (arm64): EMbC_2.0.3.tgz, r-oldrel (arm64): EMbC_2.0.3.tgz, r-release (x86_64): EMbC_2.0.3.tgz, r-oldrel (x86_64): EMbC_2.0.3.tgz
Old sources:	EMbC archive

Reverse dependencies:

Reverse suggests:

move

Linking:

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