424 citations · 459 across the 13 of their papers we have counts for
16 papers
Detecting disease progression from animal movement using hidden Markov models
Dongmin Kim, Théo Michelot, Katherine Mertes +2
Understanding disease dynamics is crucial for managing wildlife populations and assessing spillover risk to domestic animals and humans, but infection data on free-ranging animals…
Multiscale modelling of animal movement with persistent dynamics
Théo Michelot, Ephraim M. Hanks
Wild animals are commonly fitted with trackers that record their position through time, and statistical models for tracking data broadly fall into two categories: models focused on…
Understanding step selection analysis through numerical integration
Théo Michelot, Natasha J. Klappstein, Jonathan R. Potts +1
Step selection functions (SSFs) are flexible models to jointly describe animals' movement and habitat preferences. Their popularity has grown rapidly and extensions have been devel…
Continuous-time modelling of behavioural responses in animal movement
Théo Michelot, Richard Glennie, Len Thomas +2
There is great interest in ecology to understand how wild animals are affected by anthropogenic disturbances, such as sounds. Behavioural response studies are an important approach…
hmmTMB: hidden Markov models with flexible covariate effects in R
Théo Michelot
Hidden Markov models (HMMs) are widely applied in studies where a discrete-valued process of interest is observed indirectly. They have for example been used to model behaviour fro…
Varying-coefficient stochastic differential equations with applications in ecology
Théo Michelot, Richard Glennie, Catriona Harris +1
Stochastic differential equations (SDEs) are popular tools to analyse time series data in many areas, such as mathematical finance, physics, and biology. They provide a mechanistic…
Inference in MCMC step selection models
Théo Michelot, Paul G. Blackwell, Simon Chamaillé-Jammes +1
Habitat selection models are used in ecology to link the distribution of animals to environmental covariates, and identify habitats that are important for conservation. The most wi…
The Langevin diffusion as a continuous-time model of animal movement and habitat selection
Théo Michelot, Marie-Pierre Etienne, Pierre Gloaguen
1. The utilisation distribution describes the relative probability of use of a spatial unit by an animal. It is natural to think of it as the long-term consequence of the animal's…
State-switching continuous-time correlated random walks
Théo Michelot, Paul G. Blackwell
Continuous-time models have been developed to capture features of animal movement across temporal scales. In particular, one popular model is the continuous-time correlated random…
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference
Vianey Leos-Barajas, Théo Michelot
Hidden Markov models (HMMs) are popular time series model in many fields including ecology, economics and genetics. HMMs can be defined over discrete or continuous time, though her…
momentuHMM: R package for generalized hidden Markov models of animal movement
Brett T. McClintock, Theo Michelot
Discrete-time hidden Markov models (HMMs) have become an immensely popular tool for inferring latent animal behaviors from telemetry data. Here we introduce an open-source R packag…
Linking resource selection and step selection models for habitat preferences in animals
Théo Michelot, Paul G. Blackwell, Jason Matthiopoulos
The two dominant approaches for the analysis of species-habitat associations in animals have been shown to reach divergent conclusions. Models fitted from the viewpoint of an indiv…
Estimation and simulation of foraging trips in land-based marine predators
Théo Michelot, Roland Langrock, Sophie Bestley +3
The behaviour of colony-based marine predators is the focus of much research globally. Large telemetry and tracking data sets have been collected for this group of animals, and are…
Markov-switching generalized additive models
Roland Langrock, Thomas Kneib, Richard Glennie +1
We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regi…
Maximum penalized likelihood estimation in semiparametric capture-recapture models
Théo Michelot, Roland Langrock, Thomas Kneib +1
We discuss the semiparametric modeling of mark-recapture-recovery data where the temporal and/or individual variation of model parameters is explained via covariates. Typically, in…
Semiparametric stochastic volatility modelling using penalized splines
Roland Langrock, Théo Michelot, Alexander Sohn +1
Stochastic volatility (SV) models mimic many of the stylized facts attributed to time series of asset returns, while maintaining conceptual simplicity. The commonly made assumption…