The library enables probabilistic forecasting by predicting the parameters of various probability distributions, supporting use cases like prediction intervals and uncertainty quantification. It extends XGBoost with mixture density models, normalizing flows, and multi-target regression capabilities. The approach is based on Generalized Additive Models for Location, Scale and Shape (GAMLSS) framework.