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.

Lazy, structured, and efficient operations with kernel matrices.

This package contains a data structure that wraps a matrix of matrices or factorizations and acts like the matrix resulting from concatenating the input matrices without allocating further memory.

This package contains implementations of efficient representations and updating algorithms for Cholesky factorizations.

Contains implementations of efficient representations of and updating algorithms for QR factorizations.

A collection of background subtraction algorithms for spectroscopic data

Provides an implementation of lazily represented Kronecker products with efficient in-place multiplies and solves.

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LazyInverses provides a lazy wrapper for a matrix inverse, akin to Adjoint in Julia Base. See the README for example use cases.

Contains an implementation of lazily represented matrix structures that allow for the application of the Woodbury Identity.