decision-security 0.1.0a7

Decision-science utilities for security: Monte Carlo, Bayes, Survival, Value of Information, causal helpers, and viz.

Decision Security

Reusable decision-science utilities for security — Monte Carlo risk bands, Bayesian updates & calibration, survival helpers, Value of Information, light causal helpers, and visualization.

pip install decision-security 

Quickstart

import numpy as np
from decision_security.montecarlo import risk_bands, var_es, make_lognormal_severity, simulate_aggregate_losses

sev = make_lognormal_severity(meanlog=8.0, sdlog=1.2)
losses = simulate_aggregate_losses(n_periods=10000, lam=0.6, severity_sampler=sev)
print(risk_bands(losses))      # {'p50': ..., 'p90': ..., 'p95': ...}
print(var_es(losses))          # (VaR95, ES95)

Modules

•	synth: synthetic data (heavy-tail losses, counts, mixtures, survival with censoring, categorical/Dirichlet).
•	montecarlo: Poisson frequency + severity, risk bands, VaR/ES.
•	bayes: Beta-Binomial & Normal(known σ) updates, calibration helpers.
•	survival: simple Kaplan–Meier & Nelson–Aalen estimates.
•	voi: Expected Value of Perfect Information (EVPI) and simple ROI selection.
•	causal: tiny DAG utilities (parents, descendants, naive backdoor set).
•	viz: small matplotlib helpers (loss distribution, risk bands, KM curves).

Status: 0.x (APIs may change). MIT License.

See docs & examples: Security Decision Science Book and the Security Decision Labs playground.