Monte Carlo A/B Testing Library - comprehensive tools for A/B test design and analysis
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MCAB: Monte Carlo Simulations for A/B Testing
A Python toolkit to design and validate A/B experiments via simulation: estimate power, find MDE, and control type-I error. Supports iid & ratio metrics, linearization, CUPED/CUPAC variance reduction, bootstrap/permutation tests, multiple-testing corrections, and AA/AB benchmarking.
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