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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.