  Estimating sample size and statistical power is an essential part of a good
study design. This R package allows users to conduct power analysis based on
Monte Carlo simulations in settings in which consideration of the correlations
between predictors is important. It runs power analyses given a data generative
model and an inference model. It can set up a data generative model that
preserves dependence structures among variables given existing data
(continuous, binary, or ordinal) or high-level descriptions of the
associations. Users can generate power curves to assess the trade-offs between
sample size, effect size, and power of a design. This paper presents tutorials
and examples focusing on applications for environmental mixture studies when
predictors tend to be moderately to highly correlated. It easily interfaces
with several existing and newly developed analysis strategies for assessing
associations between exposures and health outcomes. However, the package is
sufficiently general to facilitate power simulations in a wide variety of
settings.
