Selection by Partitioning the Solution Paths

Documentation for package ‘SPSP’ version 0.2.0

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SPSP-package	Selection by Partitioning the Solution Paths
adalasso.glmnet	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).
adalassoCV.glmnet	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).
Fitting-Functions	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).
HighDim	A high dimensional dataset with n equals to 200 and p equals to 500.
lasso.glmnet	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).
lasso.lars	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).
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).
ridge.glmnet	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).
SCAD.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).
SPSP	Selection by partitioning the solution paths of Lasso, Adaptive Lasso, and Ridge penalized regression.
SPSP_step	The selection step with the input of the solution paths.