stelfi

stelfi fits Hawkes and log-Gaussian Cox point process models to analyze spatiotemporal patterns in ecological point-referenced data.

Key Features:

• Spatiotemporal self-exciting models: Fits point process models where events instigate others nearby, capturing self-exciting behavior in ecological data.
• Log-Gaussian Cox process models: Implements popular spatiotemporal point process models for describing the distribution of point-referenced data.
• Model extensions: Includes spatiotemporal self-excitation models and joint likelihood models to capture complex mechanisms in ecological data.
• Template Model Builder integration: Fits models using Template Model Builder through custom-written C++ templates.

Scientific Applications:

• Animal sighting analysis: Analyzes the contagious nature of animal sightings and their spatiotemporal progression, as illustrated with Sasquatch (bigfoot) sightings data within the USA.
• Ecological point pattern analysis: Enables insights into temporal and spatial progression of point pattern data typical of ecology studies.
• Beyond ecology: Implements complex spatiotemporal point process models applicable to fields beyond ecology.

Methodology:

stelfi fits models using Template Model Builder through a range of custom-written C++ templates, supporting fitting from temporal Hawkes processes to spatiotemporal self-exciting models.