wqspt

Implements a permutation test method for the weighted quantile sum (WQS) regression, building off the 'gWQS' package (Renzetti et al. (2021) ). Weighted quantile sum regression is a statistical technique to evaluate the effect of complex exposure mixtures on an outcome (Carrico et al. (2015) doi:10.1007/s13253-014-0180-3). The model features a statistical power and Type I error (i.e., false positive) rate trade-off, as there is a machine learning step to determine the weights that optimize the linear model fit. This package provides an alternative method based on a permutation test that should reliably allow for both high power and low false positive rate when utilizing WQS regression (Day et al. (2022) doi:10.1289/EHP10570).