State Space Models Python Packages

dynamax

A Python package for probabilistic state space modeling with JAX

elume

Open-source agentic memory engine for long-horizon adaptive learning. Integrates LinOSS oscillatory state-space models, attractor-based associative memory, and MemEvolve-style adaptive memory mechanisms into a single deterministic kernel.

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.

linoss-dynamics

NumPy runtime helpers for LinOSS-style oscillator dynamics — sequence stepping, closed-form damped oscillator, Bayesian filtering, MLE fit. See Roadmap issue for vision.

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.

bayesnewton

No description available

rts-smoother

High-performance RTS Kalman smoother for column-wise financial matrices (local level model)

smcjax

Sequential Monte Carlo and particle filtering in JAX

discretax

Discretax is a light weight collection of state space models implemented in JAX ⚡️

linax

Discretax is a light weight collection of state space models implemented in JAX ⚡️

lrnnx

A unified PyTorch library for Linear RNNs