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We have put a new package sparsenet on CRAN. Sparsenet fits regularization paths for sparse model selection via coordinate descent, using a penalized least-squares framework and a non-convex penalty. The package is based on our JASA paper Rahul Mazumder, Jerome Friedman and Trevor Hastie: SparseNet : Coordinate Descent with Non-Convex Penalties. (JASA 2011) http://www.stanford.edu/~hastie/Papers/Sparsenet/jasa_MFH_final.pdf We use Zhang's MC+ penalty to impose sparsity in model selection. This penalty parametrizes a family ranging between...

We have put a new package sparsenet on CRAN. Sparsenet fits regularization paths for sparse model selection via coordinate descent, using a penalized least-squares framework and a non-convex penalty. The package is based on our JASA paper Rahul Mazumder, Jerome Friedman and Trevor Hastie: SparseNet : Coordinate Descent with Non-Convex Penalties. (JASA 2011) http://www.stanford.edu/~hastie/Papers/Sparsenet/jasa_MFH_final.pdf We use Zhang's MC+ penalty to impose sparsity in model selection. This penalty parametrizes a family ranging between...

SoftImpute is a new package for matrix completion - i.e. for imputing missing values in matrices. SoftImpute was written by myself and Rahul Mazumder. softImpute uses uses squared-error loss with nuclear norm regularization - one can think of it as the "lasso" for matrix approximation - to find a low-rank approximation to the observed entries in the matrix. This low-rank approximation is then used to impute the missing entries. sof...

SoftImpute is a new package for matrix completion - i.e. for imputing missing values in matrices. SoftImpute was written by myself and Rahul Mazumder. softImpute uses uses squared-error loss with nuclear norm regularization - one can think of it as the "lasso" for matrix approximation - to find a low-rank approximation to the observed entries in the matrix. This low-rank approximation is then used to impute the missing entries. sof...