“m 2b ” package in r : Deriving multiple variables from movement data to predict behavioural states with random forests

The parameter sensitivity of random forests

(2016) Barbara F.F. Huang et al.   BMC BIOINFORMATICS

moveHMM: anRpackage for the statistical modelling of animal movement data using hidden Markov models

(2016) Théo Michelot et al.   Methods in Ecology and Evolution

Building Predictive Models inRUsing thecaretPackage

(2015) Max Kuhn   Journal of Statistical Software

Local enhancement in a seabird: reaction distances and foraging consequence of predator aggregations

(2014) Andréa Thiebault et al.   BEHAVIORAL ECOLOGY

Bird-Borne Video-Cameras Show That Seabird Movement Patterns Relate to Previously Unrevealed Proximate Environment, Not Prey

(2014) Yann Tremblay et al.   PLoS One

Hidden Markov Models: The Best Models for Forager Movements?

(2013) Rocio Joo et al.   PLoS One

Linking animal-borne video to accelerometers reveals prey capture variability

(2013) Y. Y. Watanabe et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

An experimental comparison of classification algorithms for imbalanced credit scoring data sets

(2011) Iain Brown et al.   EXPERT SYSTEMS WITH APPLICATIONS

Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines

(2009) Paula Martiskainen et al.   APPLIED ANIMAL BEHAVIOUR SCIENCE

New frontiers in biologging science

(2009) C. Rutz et al.   Biology Letters

The concept of animals' trajectories from a data analysis perspective

(2008) Clément Calenge et al.   Ecological Informatics

A movement ecology paradigm for unifying organismal movement research

(2008) R. Nathan et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA