ct
Integrated Camera-Trap Data Management and Analysis
An integrated, tidyverse-friendly workflow for camera trap data in
wildlife monitoring and ecological research. Reads and edits media metadata,
filters independent detections, analyses activity patterns and species
diversity, and estimates species density or
abundance with several methods, including the random encounter model,
camera-trap distance sampling, time-to-event, space-to-event, and the random
encounter and staying-time model (see
Rowcliffe et al. (2008)
Camera Trap R Package 
Overview
Camera traps are an essential tool for wildlife monitoring and ecological research, especially for species identification, biodiversity assessment, activity pattern analysis, occupancy modeling, density/abundance estimation, and among other.
Processing and analyzing camera trap data in R often requires multiple steps, from cleaning raw data to statistical modeling and visualization. The ct R package addresses these challenges by providing a modern, tidyverse-friendly workflow. It enables users to efficiently process data, and estimate density or abundance with several (~6) documented methods. Additionally, it integrates seamlessly with ggplot2, allowing users to generate highly customizable visualizations.
Key Features
The ct package provides a comprehensive suite of 60+ functions covering the
complete camera trap data analysis workflow. Population density estimation
is supported through Random Encounter Models, (REM), Camera Trap Distance Sampling (CTDS)
Time-To-Event (TTE), Space-To-Event (STE), Instantaneous Sampling Estimator (ISE),
and Random Encounter and Staying Time (REST/RAD-REST). Data management capabilities include
filtering independent detections, timestamp correction, and interactive spatial validation.
Community ecology functions enable activity pattern analysis, biodiversity index assessment, and
occupancy modeling input preparation. Quality control tools include detecting temporal gaps,
monitoring deployment status, and taxonomic validation.
For a full overview of all available functions, please visit the ct website
Installation:
You can install ct directly from GitHub:
# Install pak firstly if not installed
if (!requireNamespace("pak", quietly = TRUE)) {
install.packages("pak", dependencies = TRUE)
}
# Install ct from GitHub
pak::pkg_install("stangandaho/ct")
Code of conduct
Please note that this project is based on the Contributor Covenant v2.1. By participating in this project you agree to abide by its terms.
Getting help
If you encounter a clear bug, please file an issue with a minimal reproducible example. For questions and other discussion, please use relevant section.
Funding
The development of the ct package is supported by the R Consortium Infrastructure Steering Committee (ISC) under grant 25-ISC-1-04. This funding enables the creation of comprehensive statistical tools for camera trap data analysis, including population density estimation methods, and standardized data integration workflows.