# file: D:\Anti ageing research\EPISIM\episim\lab\registry.py
# hypothesis_version: 6.152.4

[-3.0, -2.2, -0.7, -0.6, -0.55, -0.35, -0.2, -0.18, -0.12, -0.08, -0.06, -0.004, 0.0, 0.002, 0.03, 0.035, 0.08, 0.12, 0.2, 0.28, 0.3, 0.32, 0.45, 0.5, 0.6, 0.7, 0.75, 0.9, 5.0, 120, 250, 400, 500, 600, 800, 1200, 1500, 2400, 2500, 2800, 3000, 3500, 4000, 5000, 8000, 20260507, 'Agent-style SEIR', 'Case-control study', 'Network contagion', 'Prospective cohort', 'agent_based_seir', 'aging', 'aging research', 'allocation_ratio', 'bandwidth', 'baseline_rate', 'beta', 'case_control', 'causal inference', 'censoring_time', 'clinical guidelines', 'clinical research', 'clinical triage', 'clinical trials', 'cluster trials', 'cluster_sd', 'cluster_size', 'cohort', 'community medicine', 'controls_per_case', 'cross_sectional', 'cutoff', 'cycles', 'decision science', 'discount', 'dynamic transmission', 'ecological', 'ecological_peai', 'economics', 'education', 'epidemiology', 'evidence synthesis', 'experimental', 'exposure_effect', 'exposure_log_odds', 'exposure_prevalence', 'gamma', 'hazard_ratio', 'health economics', 'health equity', 'health services', 'health systems', 'humanities', 'infectious disease', 'initial_infected', 'instrument_strength', 'intervention', 'intervention_period', 'level_change', 'markov_decision', 'mean_degree', 'meta_analysis', 'mixed methods', 'n', 'n_cases', 'n_clusters', 'n_days', 'n_dev', 'n_experts', 'n_external', 'n_interviews', 'n_latent_themes', 'n_nodes', 'n_periods', 'n_population', 'n_prospective', 'n_source', 'n_studies', 'n_survey', 'network science', 'network_contagion', 'observational', 'oncology', 'outcome_intercept', 'p_attrition', 'policy', 'policy evaluation', 'population health', 'pre_slope', 'prevention science', 'propensity_score', 'public health', 'qualitative research', 'quasi-experimental', 'rct_cluster', 'rct_parallel', 'recovery_probability', 'rehabilitation', 'secular_trend', 'seed_value', 'sigma', 'slope_change', 'social medicine', 'social science', 'sociology', 'stepped_wedge', 'survival_cox', 'systems science', 'tau', 'time-to-event', 'treatment_effect', 'true_effect']