m2b: Movement to Behaviour Inference using Random Forest
Prediction of behaviour from movement characteristics using observation and random forest for the analyses of movementdata in ecology.From movement information (speed, bearing...) the model predicts theobserved behaviour (movement, foraging...) using random forest. Themodel can then extrapolate behavioural information to movement datawithout direct observation of behaviours.The specificity of this method relies on the derivation of multiple predictor variables from themovement data over a range of temporal windows. This procedure allows to captureas much information as possible on the changes and variations of movement andensures the use of the random forest algorithm to its best capacity. The methodis very generic, applicable to any set of data providing movement data together withobservation of behaviour.
| Version: | 1.1.0 |
| Depends: | R (≥ 3.3.0) |
| Imports: | geosphere,caTools,ggplot2,randomForest,caret, methods, graphics, stats |
| Suggests: | adehabitatLT,moveHMM,knitr,DiagrammeR,rmarkdown |
| Published: | 2025-06-25 |
| 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 |
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