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m2b  

Movement to Behaviour Inference using Random Forest
View on CRAN: Click here


Download and install m2b package within the R console
Install from CRAN:
install.packages("m2b")

Install from Github:
library("remotes")
install_github("cran/m2b")

Install by package version:
library("remotes")
install_version("m2b", "1.0")



Attach the package and use:
library("m2b")
Maintained by
Laurent Dubroca
[Scholar Profile | Author Map]
First Published: 2017-05-03
Latest Update: 2017-05-03
Description:
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.
How to cite:
Laurent Dubroca (2017). m2b: Movement to Behaviour Inference using Random Forest. R package version 1.0, https://cran.r-project.org/web/packages/m2b. Accessed 04 May. 2025.
Previous versions and publish date:
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Complete documentation for m2b
Functions, R codes and Examples using the m2b R package
Some associated functions: dxyt . dxyt2 . extractRF . ltraj2xytb . m2b . modelRF . plot-methods . resB . resRF . shiftvalue . test . track_CAGA_005 . xytb-class . xytb-methods . xytb2hmm . xytb2ltraj . 
Some associated R codes: data.R . m2b.R . xytb-class.R .  Full m2b package functions and examples
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