---
title: "Governed end-to-end analysis pipelines"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Governed end-to-end analysis pipelines}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

The pipeline layer links import, measurement quality, preprocessing, feature construction, modeling, diagnostics, sensitivity analysis, and reporting while preserving the researcher’s declared choices. Pipeline steps are explicit functions with declared dependencies; eyeprocess does not silently choose preprocessing or statistical specifications.

```{r, eval=FALSE}
spec <- eye_analysis_spec(blink_correction="linear", pupil_baseline=c(-500,0), fixation_algorithm="ivt", aoi_rule="probabilistic")
p <- eye_analysis_pipeline(list(
  eye_pipeline_step("import", read_fun),
  eye_pipeline_step("quality", quality_fun, requires="import"),
  eye_pipeline_step("model", model_fun, requires="quality")
), spec = spec)
validate_eye_pipeline(p)
r <- run_eye_pipeline(p, context=list(path="study.csv"))
audit_eye_pipeline(r)
plot(p)
```

`eye_targets_manifest()` and `write_eye_targets_template()` provide interoperability scaffolding without pretending arbitrary closures can be losslessly translated into another pipeline engine.