SPAMS (SPArse Modeling Software) now with Python and R

Alexandre Gramfort let me know that SPAMS now has Python and R bindings. That is you can now use  SPAMS in Python and R. This is fantastic. What is SPAMS you ask? Well it is listed in several places in the Matrix Factorization Jungle page. From the main page of the project (see also here)

What is SPAMS?
SPAMS (SPArse Modeling Software) is an optimization toolbox for solving various sparse estimation problems.

• Dictionary learning and matrix factorization (NMF, sparse PCA, ...)

• Solving sparse decomposition problems with LARS, coordinate descent, OMP, SOMP, proximal methods

• Solving structured sparse decomposition problems (l1/l2, l1/linf, sparse group lasso, tree-structured regularization, structured sparsity with overlapping groups,...).

See the documentation for all the features. It is developped by Julien Mairal, with the collaboration of Francis Bach (INRIA), Jean Ponce (Ecole Normale Supérieure), Guillermo Sapiro (University of Minnesota), and Rodolphe Jenatton (INRIA). It is coded in C++ with a Matlab interface. Recently, an interface for R and Python have been developed by Jean-Paul Chieze (INRIA).License
Version 2.1 and later are open-source under licence GPLv3. It is therefore possible to redistribute the sources with any other software, as long as it under GPL licence. For other usage, please contact the authors.Related publications
You can find here some publications at the origin of this software. The "matrix factorization" and "sparse decomposition" modules were developed for the following papers:

J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding. Journal of Machine Learning Research, volume 11, pages 19-60. 2010.

J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Dictionary Learning for Sparse Coding.International Conference on Machine Learning, Montreal, Canada, 2009

The "proximal" module was developed for the following papers:

J. Mairal, R. Jenatton, G. Obozinski and F. Bach. Network Flow Algorithms for Structured Sparsity. Adv. Neural Information Processing Systems (NIPS). 2010.

R. Jenatton, J. Mairal, G. Obozinski and F. Bach. Proximal Methods for Sparse Hierarchical Dictionary Learning. International Conference on Machine Learning. 2010.

News
03/24/2012: SPAMS v2.2 is released with a Python and R interface, and new compilation scripts for a better Windows/Mac OS compatibility. 06/30/2011: SPAMS v2.1 goes open-source! 11/04/2010: SPAMS v2.0 is out for Linux and Mac OS! (Windows version and C++ interface coming soon) 02/23/2010: Windows 32 bits version available! Elastic-Net is implemented. 10/26/2009: Mac OS, 64 bits version available!

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