Encodes the four lead-paper confirmatory tests exactly per the frozen prereg §6,
calibrated on synthetic ground truth only (never re-fit to cell data):
- RQ1-P1 leak: correlation AUC + BCa CI; gate = CI excludes 0.5 (D2); 0.60 CI
lower bound = separate materiality label (material / weak-but-real), not the gate.
- RQ1-P2 padding: paired ΔAUC = AUC(no-pad) − AUC(pad) over circuits; effective
iff CI > 0.
- RQ2-P1 anonymity set: ΔH = H(federated) − H(single, matched N) with Miller-Madow
per-circuit entropy; two-sided grow / honest-shrink / inconclusive by CI sign.
- RQ2-P3 mechanism: Spearman ρ(top-3 bridge concentration, per-circuit H) + CI.
apply_holm corrects the reported RQ1/RQ2 subset against the full frozen family of
7 (family_size default 7) — never re-optimised to the 4 reported. Bootstrap
p-values order the Holm step-down only; every decision is a CI gate, never a bare p.
stats.py: bootstrap CIs can now return their resample distribution so the Holm
ordering p-value comes from the same resamples as the CI.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Turnkey implementation of the frozen prereg §6 analysis plan, written before any
confirmatory data exists (analysis-precedes-data, rigor-standards §Statistics).
Pure stdlib, no I/O, no engine, no traffic — calibrated on synthetic ground truth only:
- bootstrap_ci / two_sample_diff_ci: BCa 95% CIs (10k resamples default) with a
percentile fallback when bias/acceleration terms are degenerate; seeded and
reproducible (CIResult carries method+seed for the §6 three-seed spot-check).
- miller_madow_entropy_bits: plug-in Shannon entropy + Miller-Madow bias
correction (§3 estimator).
- spearman: rank correlation for RQ2-P3.
- holm_bonferroni: step-down multiplicity correction with explicit family_size so
a lead paper reporting a subset of the frozen 7-test family still corrects
against the full family (never re-optimised to the reported subset).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
churn.py emits a seed-deterministic kill/spawn schedule (pure data — no real VM
spin/kill; the live fabric half stays gated by the containment law). selector.py
consumes the schedule and rebuilds a circuit whenever a kill drops one of its
hops, across static | random | agent strategies; the paid frontier-model agent
arm (GOAL envelope (c)) is human-gated and NOT wired — the agent strategy here is
a local stability heuristic that spends nothing. analysis/metrics.py aggregates
the four DV families (RQ1 correlation AUC, RQ2 entropy bits, RQ3 throughput
retention + rebuild-classifier AUC) into a schema-valid, write-once metrics.json.
Acceptance check green: under a fixed churn seed the selector rebuilds every
dropped circuit (every_drop_rebuilt, all strategies) and metrics.json is
produced. Python R7 selector suite 11 passed; full SOR suite 92 passed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Land only the offline-validatable half of R7 (instrument-validation gate items
3 and 4): the detectors the gate calibrates on synthetic fixtures, never on
confirmatory-cell data. Pure stdlib functions over in-memory series — no pcaps,
no engine, no traffic, no VM fabric.
- analysis/detectors.py: shannon_entropy_bits (RQ2 anonymity-set entropy),
pearson/score_matrix/auc/linkage_auc, bridge_correlation_auc (RQ1
linkability scorer), and synthetic_bridge_fixture (seed-deterministic
known-linked / known-unlinked ground truth via the R1 SorRng).
Calibration green: entropy returns exactly log2(N) for N equiprobable senders
(gate item 4); a known-linked control pair scores AUC=1.0 and the unlinked
estimator is unbiased at chance (ensemble mean over 40 seeds = 0.498 ~ 0.5,
gate item 3). Detectors are calibrated on synthetic fixtures only — no fitting.
The traffic-moving R7 pieces (churn.py VM spin/kill, live selector rebuild loop,
metrics.json emission) are HELD for R4/R6 + a live grid and are absent here.
Python SOR suite 66 passed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
leetcrypt