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: |
1.1.0 |
| 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 |
Documentation:
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