A package for the sparse identification of nonlinear dynamical systems from data

a collection of modern sparse (regularized) linear regression algorithms.

Actually Sparse Variational Gaussian Processes implemented in GPlow

Black-box spike and slab variational inference, example with linear models

Sparse Identification of Truncation Errors (SITE) for Data-Driven Discovery of Modified Differential Equations

Physically-informed model discovery of systems with nonlinear, rational terms using the SINDy-PI method. Contains functionality for spectral filtering/differentiation.

STELA algorithm for sparsity regularized linear regression (LASSO)

Robust regression algorithm that can be used for explaining black box models (Python implementation)

Python Toolkit for Uncertainty Quantification

Automatic hyperparameter selection for Lasso-like models solving the M/EEG source localization problem

This repository contains the code used in my master thesis titled: "A state-of-the-art review of the Bouc-Wen model and hysteresis characterization through sparse regression techniques"

A Python Package for a Sparse Additive Boosting Regressor

The Python Implementation of Sparse Regression.

code for performing Bayesian ARD regression, where covariates have groups