TelemetrySpace: Fit spatial point process and geostatistical mixed effects models to electronic tagging data

Caution

As of August 2025, development of TelemetrySpace has moved to https://github.com/trackyverse/TelemetrySpace. Please direct any new issues or pull requests to that repository.

The TelemetrySpace package implements the model described in Winton et al. 2018, A spatial point process model to estimate individual centres of activity from passive acoustic telemetry data.

Installation

We suggest installing via the Ocean Tracking Network's R-universe:

install.packages('TelemetrySpace', repos = c('https://trackyverse.r-universe.dev', getOption("repos"))

The TelemetrySpace package uses Stan for model fitting. If you would like to use more-up-to-date versions of Stan or RStan dependencies, please follow the instructions at https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started.

Docker

We also offer a Docker image with all dependencies pre-installed. To use the Docker image, first install Docker. Then, you can run an R session with TelemetrySpace installed by running the following command in your terminal:

docker pull ghcr.io/trackyverse/telemetryspace:latest
docker run --rm -it \
-p 8888:8888 \
ghcr.io/trackyverse/telemetryspace:latest

To bring your data into the Docker container, you can mount a local directory to the Docker container using the -v flag. For example, to mount your ~/data directory to /data in the Docker container, you can run:

docker run --rm -it \
-p 8888:8888 \
-v $PWD/data:/data \
ghcr.io/trackyverse/telemetryspace:latest