m2b: Movement to Behaviour Inference using Random Forest

Prediction of behaviour from movement characteristics using observation and random forest for the analyses of movement data in ecology. From movement information (speed, bearing...) the model predicts the observed behaviour (movement, foraging...) using random forest. The model can then extrapolate behavioural information to movement data without direct observation of behaviours. The specificity of this method relies on the derivation of multiple predictor variables from the movement data over a range of temporal windows. This procedure allows to capture as much information as possible on the changes and variations of movement and ensures the use of the random forest algorithm to its best capacity. The method is very generic, applicable to any set of data providing movement data together with observation of behaviour.

Version:	1.0
Depends:	R (≥ 3.3.0)
Imports:	geosphere, caTools, ggplot2, randomForest, e1071, caret, methods, graphics, stats, utils
Suggests:	adehabitatLT, moveHMM, knitr, DiagrammeR, rmarkdown
Published:	2017-05-03
DOI:	10.32614/CRAN.package.m2b
Author:	Laurent Dubroca [aut, cre], Andréa Thiebault [aut]
Maintainer:	Laurent Dubroca <laurent.dubroca at gmail.com>
License:	GPL-3
URL:	https://github.com/ldbk/m2b
NeedsCompilation:	no
Materials:	README
In views:	Tracking
CRAN checks:	m2b results

Documentation:

Reference manual:	m2b.pdf
Vignettes:	m2b tutorial

Downloads:

Package source:	m2b_1.0.tar.gz
Windows binaries:	r-devel: m2b_1.0.zip, r-release: m2b_1.0.zip, r-oldrel: m2b_1.0.zip
macOS binaries:	r-release (arm64): m2b_1.0.tgz, r-oldrel (arm64): m2b_1.0.tgz, r-release (x86_64): m2b_1.0.tgz, r-oldrel (x86_64): m2b_1.0.tgz

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