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

Mambular 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

Distributional Gradient Boosting Machines

An extension of Py-Boost to probabilistic modelling

A python package for semi-structured deep distributional regression

A package for online distributional learning.

dte_adj is a Python package for estimating distribution treatment effects. It provides APIs for conducting regression adjustment to estimate precise distribution functions as well as convenient utils.

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.

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

Framework for the visualization of distributional regression models

Time Series based Ensemble Model Output Statistics

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

Penalized Transformation Models in Liesel

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

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