Functional models and algorithms for sparse signal processing

Contains a wide-ranging collection of compressed sensing and feature selection algorithms. Examples include matching pursuit algorithms, forward and backward stepwise regression, sparse Bayesian learning, and basis pursuit.

The sparse Bayesian learning sandbox

Deconvolution algorithms for diffusion MRI

Sparse Bayesian Metric Learning

Hierarchical probabilistic models for multiple gene/variant associations based on NGS data

R Package for Automatic Relevance Determination

An enhanced version of GADA, a fast and sparse segmentation algorithm