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
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