inst/extdata/methods.md

In cboettig/forecast-standards: EFI ecological forecasting output and metadata standards

Methods Description

Forecast timestep 1 day

Forecast time horizon

Data assimilation

Data Assimilation used: No If, DA used - type of method: N/A If, DA used - Number of parameters calibrated: N/A If, DA used - Sources of training data (DOI, GitHub): N/A

Model Description

Type of model (Empirical, process-based, machine learning): Empirical Model name: discrete Lotka–Volterra model Location of repository with model code: https://github.com/somewhere or https://doi.org/10.xxx Model citation: N/A Total number of model process parameters: 3

Model Covariates

Type (i.e., meteorology): N/A Source (i.e., NOAA GEFS): N/A

Uncertainty

Answers: No, Derived from data, Propagates, Assimilates

Initial conditions: Parameter: Parameter Random Effects: Process (within model): Multi-model: Driver: Scenario:

Method for propagating uncertainty (Analytic, ensemble numeric): ensemble numeric If Analytic, specific method If ensemble numeric, number of ensembles: 10

cboettig/forecast-standards documentation built on Jan. 1, 2023, 8:21 a.m.