Fit continuous-time movement models

This function fits continuous-time movement models to simulated location data using the ctmm package. It estimates movement parameters for each simulated trajectory, allowing for parallel execution. It currently supports both home range and speed estimation workflows.

Usage

fitting_models(
  obj,
  set_target = c("hr", "ctsd"),
  parallel = FALSE,
  trace = FALSE,
  ncores = parallel::detectCores(),
  ...
)

Arguments

obj

A list of simulated movement datasets, each a telemetry object compatible with ctmm R package.

set_target

A character vector specifying the research targets. Current options:

"hr"

Home range estimation.

"ctsd"

Speed & distance estimation.

parallel

Logical. If TRUE, enables parallel processing.

trace

Logical. If TRUE (default), prints progress and timing messages to the console.

ncores

Integer. Number of CPU cores to use for parallel processing. Defaults to all available cores minus one.

...

Additional arguments used internally.

Value

A list of fitted movement models, all recentered to the origin.

Details

The function generates initial parameter estimates for each dataset using ctmm::ctmm.guess(). If the data includes simulated location error, it adds an error model accordingly. Models are fitted using ctmm::ctmm.select(), which performs model selection to find the best-fit movement process. Finally, all fitted models are recentered to (0, 0) for downstream consistency.

Note

This function is intended for internal use and may assume inputs follow specific structure and constraints not referenced explicitly.

See also

ctmm::ctmm.guess(), ctmm::ctmm.select()