Package: SPSP 0.2.0.9000

SPSP: 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) <doi:10.1214/18-EJS1434>). 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.

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

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SPSP.pdf |SPSP.html✨
SPSP/json (API)
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Peer review:

Bug tracker:https://github.com/xiaoruizhu/spsp/issues

On CRAN:

feature-selectionstatisticsvariable-selection

11 exports 1 stars 2.39 score 12 dependencies 8 mentions 2 scripts 212 downloads

Last updated 10 months agofrom:c4b4778bb3. Checks:OK: 1 ERROR: 8. Indexed: yes.

Exports:adalasso.glmnetadalassoCV.glmnetadalassoCVmin.glmnetlasso.glmnetlasso.larslassoCV.glmnetMCP.ncvregridge.glmnetSCAD.ncvregSPSPSPSP_step

Dependencies:codetoolsforeachglmnetiteratorslarslatticeMatrixncvregRcppRcppEigenshapesurvival