Distributional Regression Python Packages

skpro

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

lightgbmlss

An extension of LightGBM to probabilistic modelling

xgboostlss

An extension of XGBoost to probabilistic modelling

drforest

Fast distributional random forests with CART, MMD, and sliced-Wasserstein splitting

deeptab

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.

mambular

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.

dte-adj

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.

ondil

A package for online distributional learning.

rolch

A package for online distributional learning.

insurance-distributional

Distributional GBM for insurance pricing — TweedieGBM, GammaGBM, ZIPGBM, NegBinomialGBM, per-risk volatility scoring

insurance-distributional-glm

GAMLSS for insurance pricing in Python — model variance, shape, and tail parameters as functions of covariates

stengression

Spatiotemporal engression for probabilistic forecasting on low-frequency datasets.