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lasso.glmnet() adalasso.glmnet() adalassoCV.glmnet() ridge.glmnet() lasso.lars() SCAD.ncvreg() MCP.ncvreg()
Four Fitting-Functions that can be used as an input of fitfun.SP argument to obtain the solution paths for the SPSP algorithm. The users can also customize a function to generate the solution paths. As long as the customized function take arguments x, y, family, standardize, and intercept, and return an object of class glmnet, lars (or SCAD, MCP in the future).
A high dimensional dataset with n equals to 200 and p equals to 500.
Selection by Partitioning the Solution Paths
Selection by partitioning the solution paths of Lasso, Adaptive Lasso, and Ridge penalized regression.
The selection step with the input of the solution paths.