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

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

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

Actually Sparse Variational Gaussian Processes implemented in GPlow

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

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

STELA algorithm for sparsity regularized linear regression (LASSO)

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

A Python Package for a Sparse Additive Boosting Regressor

The Python Implementation of Sparse Regression.

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"

code for performing Bayesian ARD regression, where covariates have groups