Selection by Partitioning the Solution Paths

An implementation of the feature Selection procedure by Partitioning the entire Solution Paths (namely SPSP) to identify the relevant features rather than using a single tuning parameter. By utilizing the entire solution paths, this procedure can obtain better selection accuracy than the commonly used approach of selecting only one tuning parameter based on existing criteria, cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) ). It is more stable and accurate (low false positive and false negative rates) than other variable selection approaches. In addition, it can be flexibly coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized estimators.


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0.1.0 by Xiaorui (Jeremy) Zhu, 6 months ago

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Authors: Xiaorui (Jeremy) Zhu [aut, cre] , Yang Liu [aut] , Peng Wang [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, MASS

Depends on glmnet, lars

Suggests testthat

Linking to Rcpp

See at CRAN