a Bayesian statistical support to the PoPS global pest spread model
currently using jags and the data inputs obtained for a given case study with the PoPS Global Data Acquisition notebook
- Expand on statistical tests of case study viability with temporal data
- Move towards Bayesian callibration of PoPS Global
- Benchmark model to compare performance
- Flexible option to test new modules, incorporate additional data sources, and more!
Working from a static binomial regression of model predictors to a dynamic state-space model
- Static binomial
- Dynamic (temporal) binomial
- Dynamic binomial with interaction term
- Dynamic state-space (basic)
- workspace: import packages, define case study-relevant fields
- data: import and format static and temporal data
- models: some work, some don't (listed)!
- runModel: pick a model, run it, and review intermediate outputs (convergence diagnostics and summary statistics)
- simResults: simulate and visualize results
