docs: add methodology and workflow documentation

[divine7022]

Adds documentation for the sensitivity analysis framework:

• methodology.qmd: Theoretical foundations (OAT, sobol, variance decomposition)
• workflow_documentation.qmd: Pipeline architecture and script reference
• renv_setup.qmd: Package management for reproducibility
• references.qmd

These integrate with the quarto github pages under the Docs section.

[Copilot]

Pull request overview

This PR adds four Quarto documentation files for the SIPNET sensitivity analysis framework, covering theoretical methodology, pipeline workflow, package management, and references. These files integrate with the project's Quarto-based GitHub Pages site under the Docs section.

Changes:

• Added theoretical documentation for OAT and Sobol sensitivity analysis methods
• Added pipeline architecture and script reference documentation
• Added renv package management setup guide and references page

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 3 comments.

File	Description
docs/methodology.qmd	Theoretical foundations for sensitivity analysis and variance decomposition
docs/workflow_documentation.qmd	Pipeline architecture, script execution order, and function library reference
docs/renv_setup.qmd	renv setup, SCC-specific notes, and troubleshooting guide
docs/references.qmd	Cited references for methods used in the framework

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[Copilot]

The stated formula $N(2k + 2)$ for the number of model evaluations is incorrect for the Saltelli scheme. The standard Saltelli (2002/2010) design requires $N(k + 2)$ model evaluations, not $N(2k + 2)$. This is also consistent with the formula used in workflow_documentation.qmd line 120, which correctly states $N \times (k + 2)$. The formula here should be updated to $N(k + 2)$ to match both the referenced literature and the workflow documentation.

Suggested change

	We use Saltelli's sampling design with Jansen estimators, which requires $N(2k + 2)$ model evaluations for $k$ parameters, providing efficient estimation of both $S_i$ and $T_i$.
	We use Saltelli's sampling design with Jansen estimators, which requires $N(k + 2)$ model evaluations for $k$ parameters, providing efficient estimation of both $S_i$ and $T_i$.

[Copilot]

The full title of the Saltelli et al. (2010) paper is "Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index". The truncated title "Variance based sensitivity analysis" is incomplete and could be confused with other works. Consider providing the complete title for accuracy.

Suggested change

	Saltelli, A., et al. (2010). Variance based sensitivity analysis. Computer Physics Communications.
	Saltelli, A., et al. (2010). Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications.

[Copilot]

The in-text citation references "Saltelli et al. 2008," but the references page (references.qmd) only lists a 2010 Saltelli et al. publication. Either the citation year should be updated to 2010 to match the listed reference, or the 2008 reference (Saltelli, A., et al. (2008). Global Sensitivity Analysis: The Primer. Wiley) should be added to references.qmd.

Suggested change

	This document describes the theoretical foundations and methodological approaches used in the SIPNET sensitivity analysis workflow. The framework follows established best practices in ecological forecasting (Dietze 2017) and variance-based sensitivity analysis (Saltelli et al. 2008).
	This document describes the theoretical foundations and methodological approaches used in the SIPNET sensitivity analysis workflow. The framework follows established best practices in ecological forecasting (Dietze 2017) and variance-based sensitivity analysis (Saltelli et al. 2010).