StatMixedML/XGBoostLSS: 0.1.0

Enhanced Distributional Modeling with PyTorch XGBoostLSS now fully relies on PyTorch distributions for distributional modeling. The integration with PyTorch distributions provides a more comprehensive and flexible framework for probabilistic modeling and uncertainty estimation. Users can leverage the rich set of distributional families and associated functions offered by PyTorch, allowing for a wider range of modeling options. Automatic Differentiation XGBoostLSS now fully leverages PyTorch's automatic differentiation capabilities. Automatic differentiation enables efficient and accurate computation of gradients and hessians, resulting in enhanced model performance and flexibility. Users can take advantage of automatic differentiation to easily incorporate custom loss functions into their XGBoostLSS workflows. This enhancement allows for faster experimentation and easier customization. Hyper-Parameter Optimization XGBoostLSS now enables the optimization of all XGBoost hyperparameters for enhanced modeling flexibility and performance. Bug Fixes Several minor fixes and improvements have been implemented in this release