Normalizing Flows Python Packages

zuko

Normalizing flows in PyTorch

nflows

Normalizing flows in PyTorch

flowmc

Normalizing-flow enhanced sampling package for probabilistic inference in JAX

flowjax

Easy to use distributions, bijections and normalizing flows in JAX.

glasflow

Normalising flows implemented using nflows

pyknos

Conditional density estimation with neural networks

nessai

nessai: Nested Sampling with Artificial Intelligence

pythae

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

xgboostlss

An extension of XGBoost to probabilistic modelling

lightgbmlss

An extension of LightGBM to probabilistic modelling

avitai-artifex

A research-focused modular generative modeling library built on JAX/Flax NNX

umnn

Unconstrained Monotonic Neural Networks

raylab

Reinforcement learning algorithms in RLlib

surjectors

Surjection layers for density estimation with normalizing flows

scyan

Biology-driven deep generative model for cell-type annotation in cytometry. Scyan is an interpretable model that also corrects batch-effect and can be used for debarcoding or population discovery.

linfa-vi

A Python library for variational inference with normalizing flow and annealing

lampe

Likelihood-free AMortized Posterior Estimation with PyTorch

pocomc

Preconditioned Monte Carlo

sbiax

Fast, lightweight and parallelised simulation-based inference in JAX.

vistan

Library to run VI algorithms on Stan models.

deeprob-kit

A Python Library for Deep Probabilistic Modeling

sinflow

Sliced Iterative Normalizing Flow with Minimal Dependencies

causalflows

Causal Normalizing flows in PyTorch

probaforms

Conditional normalizing flows (NFs), conditional GANs, and conditional variational autoencoders (CVAEs) with sklearn-like interface