NicholAI91/Angell-framework-real-model-outcome-testing.md: Angell Framework Real Model Outcome

Fit and test outcomes using the canonical Angell equation as a measurable model. """ Angell Framework V2: Real-model outcome testing scaffold (single-file) Author: Nicholas Reid Angell (framework) Implementation: ChatGPT (single-file scaffold) Goal Fit and test outcomes using the canonical Angell equation as a measurable model. Run baselines and ablations to produce scientific traction, not just visuals. Generate figure suite + provenance hashes so figures are evidence, not decoration. Assumptions (default) Validation target: Noma geometry-first early detection Outcome y is binary: 1 = "positive event" (e.g., early-stage identified, severe outcome, etc.) You can swap in any domain by remapping columns and interpretation. Run python angell_v2_validation.py python angell_v2_validation.py --csv your_data.csv --outdir outputs CSV schema (minimal) case_id, t, x, y Optional (recommended) rho, delay_onset_to_first_contact, delay_contact_to_referral, delay_referral_to_treatment Notes No fixed colors are set for plots (matplotlib defaults). If some libs are missing, the script will degrade gracefully where possible. Full Changelog: https://github.com/NicholAI91/Angell-framework-real-model-outcome-testing.md/commits/Real-Models