Before-After-Control-Impact (BACI) designs are widespread in environmental science, however their implicitly hierarchical nature complicates the evaluation of statistical power. Here, we describe epower, an r package for assessing statistical power of BACI designs. The package uses Bayesian statistical methods via the r-package INLA to fit the appropriate hierarchical model to user supplied pilot survey data. A posterior sample is then used to build a Monte Carlo simulation to test statistical power specifically for the Before/After × Control/Impact interaction term in the BACI model. Power can be assessed for any number of user-specified effect sizes for the existing design, or across a range of levels of replication for any part of the sampling design hierarchy. The package offers a user friendly robust approach for assessing statistical power of BACI designs whilst accounting for uncertainty in parameter values within a fully generalized framework.