distributional-regression

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

DeepTab is a Python package that simplifies tabular deep learning by providing a suite of models for regression, classification, and distributional regression tasks. It includes models such as Mambular, TabM, FT-Transformer, TabulaRNN, TabTransformer, and tabular ResNets.

ggplot-based graphics and useful functions for GAMs fitted using the mgcv package

An extension of CatBoost to probabilistic modelling

A package for online distributional learning.

Distributional Gradient Boosting Machines

Interpretable additive models for tabular data: classical GAMs (with REML smoothness selection, inference, shape constraints & diagnostics) plus neural additive models (NAM, NBM, NodeGAM, GPNAM, SIAN, SPAM, and more) for regression, classification, and distributional regression.

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. (2025)

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

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

Generalized Latent Variable Models for Location, Shape, and Scale (GLVM-LSS)

Time Series based Ensemble Model Output Statistics

Framework for the visualization of distributional regression models

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

Reproduction code for the paper on online multivariate distributional regression for electricity price forecasting

Multivariate conditional density estimation combining semi-parametric statistical modeling and neural network-based normalizing flows for enhanced interpretability.