R: Selection by Partitioning the Solution Paths

# Selection by Partitioning the Solution Paths

## Documentation for package ‘SPSP’ version 0.2.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

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.