## Function reference (short)

### srh.kway.full()
One-call pipeline: ANOVA on ranks, descriptives, post hocs, simple effects, and design diagnostics.

Syntax:
```r
srh.kway.full(y ~ A + B (+ C ...), data, max_levels = 30, type = 2, scope = c("within", "global"))
```

Returns a list: `anova`, `summary`, `posthoc_cells`, `posthoc_simple`, `meta`.

- 1 factor: `srh.kway()`
- 2 factors with `type = 2`: `srh.effsize()`
- 2 factors with `type = 3`: `srh.kway()`
- 3+ factors: `srh.kway()`

### write.srh.kway.full.tsv()
Export the full result to a single TSV.

Syntax:
```r
write.srh.kway.full.tsv(obj, file = "srh_kway_full.tsv", sep = "\t", na = "", dec = ".")
```

`dec` can be `"."` or `","`; numeric output has no scientific notation; character tables are normalized for `dec = ","`; `META` includes scope, design diagnostics, warnings, and call when available.

### srh.kway()
K-way SRH-style ANOVA on ranks with tie-corrected p-values and rank-based effect sizes.

### srh.effsize()
Two-factor SRH with effect sizes (`eta2H`, `eps2H`) from H.

### nonpar.datatable()
Compact descriptives (global mean ranks, medians, quartiles, IQR) for all main effects and interactions.

### srh.posthoc()
Dunn-Bonferroni pairwise matrices for one specified effect. Options: `method`, `digits`, `triangular`, `numeric`, `sep`.

### srh.posthocs()
Dunn-Bonferroni pairwise matrices for all effects. Returns a named list with `P.adj` tables for `A`, `B`, `A:B`, etc.

### srh.simple.posthoc()
Simple-effects post hocs within levels of conditioning factors. `compare` selects the target factor; `scope = "within"` or `"global"`.

### srh.simple.posthocs()
Enumerates all simple-effect configurations for a design. Returns a named list like `COMPARE(gender) | BY(condition x age_cat)`.

### as_jamovi_srh_full()
Normalizes `srh.kway.full()` output into a stable Jamovi/backend-ready list structure.

### normality.datatable()
Shapiro-Wilk normality per subgroup for all factor combinations; returns `W`, `p`, `OK/NOT OK`.

### residuals.normality.datatable()
Shapiro-Wilk on global residuals from an ANOVA fitted to the selected factors; one test per model.

### residuals.cellwise.normality.datatable()
Shapiro-Wilk on model residuals within each cell (matches classical ANOVA per-cell error normality).

### balance.chisq.datatable()
Count-balance checks: homogeneity (1 factor), independence (2 factors), log-linear independence (3+); `ChiSq`, `df`, `p`, `OK/NOT OK`.

### levene.plan.datatable()
Levene/Brown-Forsythe for full-plan cells (highest-order interaction); `F`, `df`, `p`, `OK/NOT OK`.

### plan.diagnostics()
One-call diagnostics: raw normality, residuals cellwise normality, Levene (median), balance chi-square; prints an overall summary and returns full tables.
