PyTorch Implementation for Deep Metric Learning Pipelines

Fit models to data from unmarked animals using Stan. Uses a similar interface to the R package 'unmarked', while providing the advantages of Bayesian inference and allowing estimation of random effects.

This package provides routines for parameter estimation and model diagnostics for any probability density or mass function implemented in R via maximum likelihood given a data set, with or without covariates. Tools in this package have general applicability, especially in survival analysis and distance sampling. 🔍💻

Line-transect Distance Sampling Shiny App

Python module for automation of Distance Sampling analyses with Distance software (http://distancesampling.org/)

🦝 experimental camera based distance sampling (population study)

This repository contains code to perform the wildlife density modelling approach outlined in Houldcroft et al. (2024).

Farr, M.T., Green, D.S., Holekamp, K.E., Roloff, G.J., & Zipkin, E.F. 2019. Multi-species hierarchical modeling reveals variable responses of African carnivores to management alternatives. Ecological Applications 9(2): e01845.

Farr, M.T., Green, D.S., Holekamp, K.E., Roloff, G.J., & Zipkin, E.F. 2019. Multi-species hierarchical modeling reveals variable responses of African carnivores to management alternatives. Ecological Applications 9(2): e01845.