# @phoebe/design

Optimal experimental design and adaptive-trial machinery for Phoebe. Provides
Bayesian Optimal Experimental Design (exact, Nested-Monte-Carlo, and PCE
lower-bound Expected Information Gain), grid-Bayesian Adaptive Design
Optimization (ADO) over psychometric, delay-discounting, and risk models,
amortized greedy-BOED policies that serve designs in O(1), group-sequential
efficacy boundaries (Lan-DeMets O'Brien-Fleming / Pocock spending and
Haybittle-Peto), response-adaptive randomization (Thompson & RSIHR), BOIN
dose-finding boundaries, predictive-probability futility, master-protocol
(platform/basket/umbrella) operating-characteristic simulation, and SMART
designs with backward-induction Q-learning value estimation of dynamic treatment
regimes. All algorithms are deterministic under a seeded LCG PRNG — `Date.now`
and `Math.random` are never used.