Data Analysis & Modeling

Our expertise covers data analysis applied to ecology, including population dynamics (e.g., count data, capture-mark-recapture), eco-epidemiology (e.g., SEIR models, survival analysis), and ecotoxicology (e.g., dose-response, toxicokinetic-toxicodynamic modeling).

We employ a range of statistical techniques—from classical methods (hypothesis testing, generalized additive or linear mixed models) to complex hierarchical Bayesian inference models.

Lectures

A set of Jupyter Notebooks, deployable for you on JupyterHub, covering various Ecological Modeling domains (differential equations, SEIR models, sensitivity analysis, dispersion models), and an Advanced Ecotoxicological Modelling course introducing Bayesian inference (from LM to GLMM), TK-TD models and their inference—including GUTS (Generalized Unified Threshold models of Survival)—as well as DEB theory (Dynamic Energy Budget).

Sofwares

morse: Bayesian inference and analysis of survival and reproduction data
rPBK: Bayesian inference for PB-TK (Physiologically Based ToxicoKinetics) models.
rbioacc : Bayesian inference for multi-compartment, multi-compound bioaccumulation models..

Scenario Modeling

We have recognized expertise in scenario modeling for eco-epidemiology and ecotoxicology (PBK, TK-TD, DEB), as well as multi-agent models.

These projects are custom-built and have resulted in peer-reviewed research publications.