Final preparations for cchsflow v3

.Rbuildignore

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 ^CRAN-RELEASE$
 ^CODE_OF_CONDUCT.md
 ^.github
+^papers$
+^\.claude$
+^ceps$
+^.*\.DS_Store$

.claude/skills/cchsflow-derive/SKILL.md

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+---
+name: cchsflow-derive
+description: Write and review derived variable functions for cchsflow. Use when implementing new DV functions (calculate_*, assess_*, categorize_*), upgrading existing functions to v3 architecture, reviewing DV code for correctness, or preparing DV changes for commit. Covers the 3-step architecture, source-agnostic design, quality tiers, patterns, testing, and package-level validation.
+allowed-tools: Bash(Rscript:*), Bash(R:*), Bash(git:*), Read, Glob, Grep
+---
+
+# cchsflow derived variable development
+
+Write, review, and validate derived variable functions using the v3 3-step architecture.
+
+## Usage
+
+```
+/cchsflow-derive                    # general guidance (reads foundations)
+/cchsflow-derive calculate_bmi      # review/write a specific function
+/cchsflow-derive --check            # run done criteria checks
+```
+
+## Before you start
+
+### Required reading
+
+Before writing or reviewing a DV function, read these docs (in this skill's `docs/` folder):
+
+1. **[foundations.md](docs/foundations.md)** — 3-step architecture, missing data handling, quality tiers, coding standards, anti-patterns. Read this first.
+2. **The pattern doc** that matches your function (see "Choose a pattern" below)
+
+### Choose a pattern
+
+Identify which pattern your function follows, then read the corresponding doc:
+
+| Pattern | When to use | Doc |
+|---------|-------------|-----|
+| **Formula calculation** | Compute a value from inputs (BMI, pack-years) | [formula-calculation.md](docs/patterns/formula-calculation.md) |
+| **Category grouping** | Map values to categories (BMI categories, smoking status) | [category-grouping.md](docs/patterns/category-grouping.md) |
+| **Pass-through** | Clean and forward a single variable | [pass-through.md](docs/patterns/pass-through.md) |
+| **Cat-to-continuous** | Midpoint imputation from categorical ranges | [cat-to-continuous.md](docs/patterns/cat-to-continuous.md) |
+| **Multi-source routing** | Choose best source with priority chain | [multi-source-routing.md](docs/patterns/multi-source-routing.md) |
+| **Pathway branching** | Complex decision tree with gate variables | [pathway-branching.md](docs/patterns/pathway-branching.md) |
+
+### Reference material
+
+- **[7-levels.md](docs/7-levels.md)** — function complexity taxonomy (L1-L7)
+- **[function-inventory.md](docs/function-inventory.md)** — all existing DV functions with pattern, level, and tier
+- **[testing.md](docs/testing.md)** — unit test and golden fixture patterns, common failure diagnostics
+
+## Development workflow
+
+### 1. Write tests
+
+Follow the test tier matching your function's quality tier (see [testing.md](docs/testing.md)):
+
+- **Bronze**: Happy path + one missing input
+- **Silver**: + out-of-range, vectors, dataframe via `mutate()`
+- **Gold**: + every `case_when()` branch, tagged NA type verification, `output_format` parameter
+
+### 2. Write the function
+
+Follow the pattern template from the appropriate pattern doc. Key principles:
+
+- **Source-agnostic**: Semantic parameter names (`height_m`, `weight_kg`), not CCHS variable names. ONE function for both PUMF and Master; the worksheet routes different source variables to the same parameters.
+- **3-step**: `clean_variables(output_format = "tagged_na")` → `case_when()` logic → `clean_variables(output_format = output_format)`
+- **Step 1 always uses `"tagged_na"`**: Never pass the user's `output_format` to Step 1 — `any_missing()` in Step 2 won't detect numeric missing codes.
+- **Namespace-qualify**: `dplyr::case_when()`, `haven::tagged_na()` — functions must work standalone.
+
+### 3. Write roxygen documentation
+
+Silver and gold tier require the full template (see foundations.md § Documentation):
+
+```r
+#' @title [verb phrase]
+#' @description [1-2 sentences]
+#' @details [implementation notes, PUMF vs Master table if source-agnostic]
+#' @param var1 [description]
+#' @param output_format Output missing data format: "tagged_na" (default) or "original".
+#' @param ... Arguments passed from deprecated aliases.
+#' @return [type and range]
+#' @examples
+#' # Scalar
+#' # Vector
+#' # Dataframe
+#' # Standalone with rec_with_table (in \dontrun{})
+#' @references
+#' @seealso
+#' @export
+```
+
+**`@param ...` rule**: If deprecated aliases use `@rdname` pointing to your function and their signature is `function(...)`, you MUST add `@param ... Arguments passed from deprecated aliases.` to your roxygen. Otherwise R CMD check will report "Undocumented arguments in Rd file: '...'".
+
+### 4. Write deprecated aliases (if renaming)
+
+If the function replaces an older function name, add aliases in `R/deprecated-aliases.R`:
+
+```r
+#' @rdname new_function_name
+#' @export
+old_function_name <- function(...) {
+  .Deprecated("new_function_name",
+    msg = "old_function_name() is deprecated. Use new_function_name() instead.")
+  new_function_name(...)
+}
+```
+
+### 5. Update worksheets (if needed)
+
+If the function is referenced from `variable_details.csv` via `Func::`:
+
+- Update `recEnd` to point to the new function name
+- Update `dummyVariable` if function name changed
+- Run `Rscript exec/fix-worksheets.R` after any CSV modification
+- Rebuild RData if worksheet structure changed (see cchsflow-worksheets skill)
+
+## Done criteria
+
+**Before committing DV function changes, ALL of these must pass.** Run them in order — earlier checks are faster and catch different issues.
+
+### Check 1: Unit tests pass
+
+```r
+# From the project root (or worktree root)
+Rscript -e 'devtools::load_all(); testthat::test_file("tests/testthat/test-<domain>.R")'
+```
+
+Verify: 0 failures for in-scope tests. Pre-existing failures in other test files are acceptable (note them but don't block on them).
+
+### Check 2: R CMD check passes
+
+```r
+# Quick check — catches NAMESPACE, roxygen, imports (skips tests/examples)
+Rscript -e 'devtools::check(document = FALSE, args = "--no-tests --no-examples --no-vignettes --no-manual")'
+
+# Full check — recommended before PR
+Rscript -e 'devtools::check()'
+```
+
+Verify: 0 **new** errors/warnings/notes compared to the branch baseline. Common issues caught only here:
+
+- Undocumented `...` from `@rdname` aliases
+- Missing NAMESPACE exports
+- Broken `@examples`
+- Undeclared imports in DESCRIPTION
+
+### Check 3: Worksheet validation (if worksheets changed)
+
+Invoke the `cchsflow-validation` skill, or run manually:
+
+```r
+Rscript exec/fix-worksheets.R
+```
+
+### Check 4: Roxygen checklist
+
+Verify manually against the template in Step 3 above:
+
+- [ ] `@title`, `@description`, `@details` present
+- [ ] All `@param` documented (including `...` if aliases exist)
+- [ ] `@examples` includes scalar, vector, dataframe, and `rec_with_table()`
+- [ ] `@return` describes type and range
+- [ ] `@export` present
+- [ ] `@seealso` links related functions
+
+### Check 5: Test coverage checklist
+
+- [ ] Every `case_when()` branch has a test
+- [ ] Scalar, vector, and dataframe inputs tested
+- [ ] Missing inputs tested (NA, tagged_na("a"), tagged_na("b"))
+- [ ] Boundary values tested (for categorization functions)
+- [ ] Deprecated aliases tested (expect deprecation warning + correct delegation)
+
+## Cross-references
+
+### Related cchsflow skills
+
+- **cchsflow-review** — PR review of worksheet changes (L0-L6 process). Lives on `skills/review-validation` branch.
+- **cchsflow-validation** — programmatic worksheet validation. Lives on `skills/review-validation` branch.
+- **cchsflow-worksheets** — worksheet authoring guidance. Lives on `skills/review-validation` branch.
+
+### External references
+
+- R CMD check guidance: `~/github/ai-infrastructure/context/domains/r_packages.md` § "Local verification before committing"
+- V3 coding standards: project memory `project_derive_function_standards.md`
+- Reference implementations: `bmi_fun()` in `R/bmi.R` (formula), `pack_years_fun()` in `R/smoking.R` (complex)

.claude/skills/cchsflow-derive/docs/7-levels.md

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+# Function levels (L1-L7)
+
+A taxonomy of reusable function complexity. Higher levels compose lower
+levels. Understanding the level helps you write the right amount of code
+and reuse existing infrastructure.
+
+## Level definitions
+
+| Level | Name | Purpose | Example |
+|-------|------|---------|---------|
+| L1 | Foundational utility | Low-level missing data, cleaning, pattern detection | `any_missing()`, `clean_variables()`, `assign_missing()` |
+| L2 | Midpoint mapping | Convert categorical ranges to continuous values via lookup table | `smkg_age_midpoint()` |
+| L3 | Single-source pass-through | Wrap and clean a single input, worksheet handles routing | `calculate_age_start_smoking()` |
+| L4 | Categorical-to-continuous conversion | Apply midpoint imputation with domain logic | `calculate_SMK_06A_cont()` |
+| L5 | Filter/route by status | Extract subset of input based on status filtering | `calculate_SMKG203_cont()`, `assess_quit_pathway()` |
+| L6 | Multi-source combining | Route multiple sources with priority hierarchy | `calculate_time_quit_smoking_complete()` |
+| L7 | Complex multi-source unification | Full decision tree combining multiple inputs | `calculate_SMKDSTY_cat6()`, `calculate_pack_years()` |
+
+## Decision tree
+
+Use this to classify your function:
+
+```
+Does your function just pass through a single source?
+  → YES → L3 (pass-through)
+  → NO ↓
+
+Does it convert categories to continuous values?
+  → YES, using a lookup table only → L2 (midpoint mapping)
+  → YES, with domain logic → L4 (cat-to-continuous)
+  → NO ↓
+
+Does it filter/extract based on a status variable?
+  → YES, single source filtered by status → L5 (filter/route)
+  → NO ↓
+
+Does it combine multiple sources with priority?
+  → YES, with pathway-aware routing → L6 (combining)
+  → NO ↓
+
+Does it have a complex decision tree with multiple inputs?
+  → YES → L7 (complex unification)
+```
+
+## How levels compose
+
+Pack-years demonstrates the full stack:
+
+```
+calculate_pack_years (L7)
+├── clean_variables() (L1)
+├── any_missing() + get_priority_missing() (L1)
+├── SMKDSTY_A (L7: calculate_SMKDSTY_cat6)
+├── age_start_smoking (L3: calculate_age_start_smoking)
+│   └── derive_passthrough() (L1)
+├── time_quit_smoking (L6: calculate_time_quit_smoking_complete)
+│   ├── calculate_SMK_06A_cont() (L4)
+│   │   └── smkg_age_midpoint() (L2)
+│   └── pathway logic with SMK_10_gate (L5: assess_quit_pathway)
+├── cigs_per_day (L7: calculate_cigs_per_day)
+│   └── status-based routing (L5 pattern)
+└── age (L3: via worksheet routing)
+```
+
+## Level-by-level guidance
+
+### L1: Foundational utilities
+
+These are shared infrastructure. You rarely write new L1 functions — you
+use them. Key functions to know:
+
+- `clean_variables(vars, variable_details, output_format)` — step 1 and 3
+- `any_missing(var1, var2, ...)` — vectorised missing detection
+- `get_priority_missing(var1, var2, ...)` — NA::b wins over NA::a
+- `assign_missing(type, var_name, variable_details)` — create typed missing
+- `derive_passthrough(value, variable_name, variable_details, output_format)` — L3 helper
+
+### L2: Midpoint mapping
+
+A lookup table that converts categorical codes to continuous values.
+Typically a simple named vector or small helper function.
+
+```r
+smkg_age_midpoint <- function(category) {
+  midpoints <- c(8, 13, 16, 18.5, 22, 27, 32, 37, 42, 47, 55)
+  midpoints[category]
+}
+```
+
+### L3: Single-source pass-through
+
+Minimal wrapper around `derive_passthrough()`. The worksheet handles
+which source variable to feed in.
+
+```r
+calculate_age_start_smoking <- function(
+    age_start_smoking, variable_details = NULL, output_format = "tagged_na") {
+  derive_passthrough(age_start_smoking, "age_start_smoking",
+                     variable_details, output_format)
+}
+```
+
+### L4-L7: See pattern docs
+
+These levels correspond to specific patterns:
+
+- L4 → `patterns/cat-to-continuous.md`
+- L5 → `patterns/multi-source-routing.md` (filter variant)
+- L6 → `patterns/multi-source-routing.md` or `patterns/pathway-branching.md`
+- L7 → `patterns/formula-calculation.md` or `patterns/category-grouping.md`
+
+## Existing function inventory
+
+See `function-inventory.md` for a complete mapping of all current DV
+functions to their levels and patterns.