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Estimating sample size and statistical power is an essential part of a goodstudy design. This R package allows users to conduct power analysis based onMonte Carlo simulations in settings in which consideration of the correlationsbetween predictors is important. It runs power analyses given a data generativemodel and an inference model. It can set up a data generative model thatpreserves dependence structures among variables given existing data(continuous, binary, or ordinal) or high-level descriptions of theassociations. Users can generate power curves to assess the trade-offs betweensample size, effect size, and power of a design. This paper presents tutorialsand examples focusing on applications for environmental mixture studies whenpredictors tend to be moderately to highly correlated. It easily interfaceswith several existing and newly developed analysis strategies for assessingassociations between exposures and health outcomes. However, the package issufficiently general to facilitate power simulations in a wide variety ofsettings.

mpower: An R Package for Power Analysis via Simulation for Correlated Data