Realistic Confidence Intervals for Non-Stationary Extreme Value Statistics // This framework provides versatile algorithms to efficiently infer confidence intervals for extreme value statistics, such as extreme quantiles and return levels, that are representative of the asymmetric uncertainty spread, using extreme value theory extrapolation and the profile likelihood (see e.g., Coles (2001) doi:10.1007/978-1-4471-3675-0). Unlike existing algorithms, the CI endpoints are found without the need for a strict prespecified range, can be covariate- dependent, and can be based on weighted samples. This package is motivated by Zeder et al. (2023) doi:10.1029/2023GL104090 and by Pasche et al. (2026) doi:10.1007/s10687-026-00536-9.