Fit Sparse Linear Regression Models via Nonconvex Optimization

Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010). Implements the methodology described in Mazumder, Friedman and Hastie (2011) . Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.


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Reference manual

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install.packages("sparsenet")

1.3 by Trevor Hastie, 4 months ago


http://www.stanford.edu/~hastie/Papers/Sparsenet/jasa_MFH_final.pdf


Browse source code at https://github.com/cran/sparsenet


Authors: Rahul Mazumder [aut, cre] , Trevor Hastie [aut, cre] , Jerome Friedman [aut, cre]


Documentation:   PDF Manual  


GPL-2 license


Imports methods

Depends on glmnet, Matrix, shape


See at CRAN