# 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.
```bash
pip install decision-security
```
## Quickstart
```python
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.
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