An extension of XGBoost to probabilistic modelling

An extension of LightGBM to probabilistic modelling

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

An extension of CatBoost to probabilistic modelling

Mambular is a Python package that brings the power of Mamba architectures to tabular data, offering a suite of deep learning models for regression, classification, and distributional regression tasks. This includes models like Mambular, FT-Transformer, TabTransformer and tabular ResNets.

Distributional Gradient Boosting Machines

An extension of Py-Boost to probabilistic modelling

A python package for semi-structured deep distributional regression

Code of "Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts", Pic et al. (2024+)

Code for the KDD 2019 workshop paper. Attention mechanism for distribution regression.

Time Series based Ensemble Model Output Statistics

Framework for the visualization of distributional regression models

code for the KDD 2019 workshop paper https://arxiv.org/abs/1904.10583. Kernel mean embedding for distribution regression.

Penalized Transformation Models in Liesel

Bayesian Conditional Transformation Models by Manuel Carlan, Thomas Kneib and Nadja Klein