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  "Title": "Selection by Partitioning the Solution Paths",
  "Description": "An implementation of the feature Selection procedure by\nPartitioning the entire Solution Paths (namely SPSP) to\nidentify the relevant features rather than using a single\ntuning parameter. By utilizing the entire solution paths, this\nprocedure can obtain better selection accuracy than the\ncommonly used approach of selecting only one tuning parameter\nbased on existing criteria, cross-validation (CV), generalized\nCV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018)\n<doi:10.1214/18-EJS1434>). It is more stable and accurate (low\nfalse positive and false negative rates) than other variable\nselection approaches. In addition, it can be flexibly coupled\nwith the solution paths of Lasso, adaptive Lasso, ridge\nregression, and other penalized estimators.",
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  "Date": "2023-10-21",
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  "BugReports": "https://github.com/XiaoruiZhu/SPSP/issues",
  "License": "GPL (>= 2)",
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  "Repository": "https://xiaoruizhu.r-universe.dev",
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    "statistics",
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      "title": "Selection by Partitioning the Solution Paths",
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      ]
    },
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      "title": "Nine Fitting-Functions that can be used as an input of 'fitfun.SP' argument to obtain the solution paths for the SPSP algorithm.",
      "topics": [
        "adalasso.glmnet",
        "adalassoCV.glmnet",
        "adalassoCVmin.glmnet",
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        "lasso.glmnet",
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        "ridge.glmnet",
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      "title": "Selection by partitioning the solution paths of Lasso, Adaptive Lasso, and Ridge penalized regression.",
      "topics": [
        "SPSP"
      ]
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      "title": "The selection step with the input of the solution paths.",
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