Method name
Table of Contents
Description & principle
A clear, technical yet accessible explanation of the method, its core principle(s).
Major variants
optional
If the method has variants that seem important, either already widespread or promising and well documented.
Further online resources
References to useful online resources to get started, e.g. explanation blogs
Reference articles
Method
- One or a few key academic references that introduce or formalize the method.
Research applications
With RS data in Ecology / Biodiversity
- A
Without RS data (Ecology domain)
optional
- B
R / Python Packages
- Name of packages, link to official pages
- Short note on usage & documentation (optional)
Code Cells
optional
Template code cells or GitHub Gist links.
Assessment table
| Category | Criteria | Assessment |
|---|---|---|
| Outcome | Objective | automatically filled |
| Estimand | ||
| Validity | ||
| Data compatibility | Type | |
| Required TS length | ||
| Handles few samples | ||
| Handles huge datasets (n) | ||
| Handles missing data | ||
| RS-data proven | ||
| Assumptions | Functional form | |
| No unobserved confounders | ||
| No interference | ||
| Well-defined treatments | ||
| Common support (positivity) | ||
| Causal Markov Condition | ||
| Faithfulness | ||
| IDD | ||
| Model specific assumption | ||
| Model properties | Requires explicit processes | |
| Exposure type | ||
| Number of variables | ||
| Handles lag effects | ||
| Propagates uncertainty | ||
| Parametric nature | ||
| Packages | Language | |
| Usage |