---
title: "Retention-first Bayesian SCR responsivity"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Retention-first Bayesian SCR responsivity}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

`estimate_scr_responsivity_bayes()` treats SCR responsivity as graded evidence rather than a binary preprocessing deletion rule. It keeps the conventional median-amplitude non-responder flag, estimates a Beta-Binomial posterior response probability, and retains every participant with finite trial data for model-based sensitivity analyses.

This supports a primary-model/robustness workflow in which hard exclusion can be compared with hierarchical retention. The implementation is deliberately transparent and dependency-light; it does not claim to reproduce a Dirichlet-process mixture model.

Methodological motivation includes Thomas & Rabinak (2026), DOI 10.1016/j.biopsycho.2026.109336.