Add rfaR manual skeleton

rfaR-book/.gitignore

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+/.quarto/
+**/*.quarto_ipynb

rfaR-book/01-introduction.qmd

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+# Introduction
+
+## Purpose
+
+`rfaR` is an R implementation of the Risk Management Center Reservoir
+Frequency Analysis (RMC-RFA) software. RMC-RFA (and `rfaR`) use apply stratified
+monte carlo methods to deterministic flood routing to produce a hydrologic hazard
+curve[^1] at individual dam and levee sites. Hazard analyses support risk assessments
+within the USACE Dam & Levee Safety Program.
+
+[^1]: In risk analysis, the *hazard* is the risk source where potential consequences relate to harm [@aven_society_nodate]. In the context of dam and levee risk analyses, the source of risk is often defined using a reservoir stage-frequency relationship. The terms *hydrologic loading*, *stage-frequency*, and *hydrologic hazard* are used interchangeably to relate reservoir/river stages to annual exceedance probabilities (AEPs). These relationships are commonly presented as curves and are used as probabilistic loading conditions in dam and levee risk analyses.
+
+
+## rfaR Analysis Summary
+
+`rfaR` combines deterministic flood routing (Modified Puls) with a
+nested, two-loop Monte Carlo framework to quantify:
+
+- **Natural variability** — simulated in the inner loop as a *realization*. Each
+  realization represents 10,000 indiviual flood events with randomly sampled
+  starting stage, hydrograph shape, and inflow volume (from an associated frequency distribution).
+
+- **Knowledge uncertainty (epistemic uncertainty)** — simulated in the outer loop, by sampling
+  different parameter sets (from LP3 or GEV flood frequency distributions,
+  informed by MCMC output from RMC-BestFit).
+
+## Relationship to RMC-RFA
+
+`rfaR` reimplements the core RMC-RFA methodology in open-source R, offering:
+
+- Reproducibility and transparency in the routing and sampling logic
+- Integration with the broader R/RMC ecosystem (e.g., RMC-BestFit outputs)
+
+## How This Manual Is Organized
+
+- **Methodology** — statistical and hydrologic methods behind the two-loop
+  simulation framework
+- **Installation** — getting `rfaR` running locally
+- **Workflow** — an end-to-end walkthrough from raw inputs to a
+  stage-frequency curve
+- **Simulation Modes** — Median, Expected, and Full Uncertainty simulation modes explained
+- **Function Reference** — Function references (auto-generated from package documentation)
+- **Validation** — comparison against RMC-RFA / HEC-HMS benchmark results
+
+For function-level documentation, also see the
+[pkgdown reference site](https://usace-rmc.github.io/rfaR/).

rfaR-book/02-methodology.qmd

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+# Methodology
+
+## Overview
+
+`rfaR` implements a two-loop, nested Monte Carlo framework to propagate both
+natural variability and knowledge uncertainty through a deterministic flood
+routing model.
+
+## Outer Loop: Parameter Uncertainty
+
+
+
+## Inner Loop: Realizations
+
+
+
+## Flood Routing

rfaR-book/03-installation.qmd

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+# Installation
+
+## Requirements
+
+- R (version ≥ *TODO*)
+- Recommended: [Positron](https://positron.posit.co/) or RStudio
+
+## Installing rfaR
+
+```r
+# From GitHub
+remotes::install_github("USACE-RMC/rfaR")
+```
+
+*TODO: note CRAN install instructions once published.*
+
+## Dependencies
+
+*TODO: list key dependencies (ggplot2, future/future.apply, lmom, cli, etc.)
+and any that require special handling on USACE machines.*
+
+## Verifying Installation
+
+```r
+library(rfaR)
+# TODO: smoke-test
+```

rfaR-book/04-workflow.qmd

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+# Workflow
+
+## Overview
+
+
+## Step 1: Prepare Inputs
+
+
+## Step 2: Hydrograph Setup
+
+
+## Step 3: Sampling
+
+
+
+## Step 4: Routing
+
+
+## Step 5: Running the Full Simulation
+
+
+## Step 6: Interpreting Results