{
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  "Package": "ncpen",
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  "Title": "Unified Algorithm for Non-Convex Penalized Estimation for\nGeneralized Linear Models",
  "Version": "1.0.1",
  "Date": "2026-08-10",
  "Authors@R": "c(person(\"Dongshin\", \"Kim\", email = \"dongshin.kim@outlook.com\",role = c(\"aut\", \"cre\", \"cph\")),\nperson(\"Sunghoon\", \"Kwon\", email = \"shkwon0522@gmail.com\",role = c(\"aut\", \"cph\")),\nperson(\"Sangin\", \"Lee\", email = \"sanginlee44@gmail.com\",role = c(\"aut\", \"cph\")))",
  "Description": "An efficient unified nonconvex penalized estimation\nalgorithm for Gaussian (linear), binomial Logit (logistic),\nPoisson, multinomial Logit, and Cox proportional hazard\nregression models. The unified algorithm is implemented based\non the convex concave procedure and the algorithm can be\napplied to most of the existing nonconvex penalties. The\nalgorithm also supports convex penalty: least absolute\nshrinkage and selection operator (LASSO). Supported nonconvex\npenalties include smoothly clipped absolute deviation (SCAD),\nminimax concave penalty (MCP), truncated LASSO penalty (TLP),\nclipped LASSO (CLASSO), sparse ridge (SRIDGE), modified bridge\n(MBRIDGE) and modified log (MLOG). For high-dimensional data\n(data set with many variables), the algorithm selects relevant\nvariables producing a parsimonious regression model. Kim, D.,\nLee, S. and Kwon, S. (2021) <doi:10.32614/RJ-2021-003>, Lee,\nS., Kwon, S. and Kim, Y. (2016)\n<doi:10.1016/j.csda.2015.08.019>, Kwon, S., Lee, S. and Kim, Y.\n(2015) <doi:10.1016/j.csda.2015.07.001>. (This research is\nfunded by Julian Virtue Professorship from Center for Applied\nResearch at Pepperdine Graziadio Business School and the\nNational Research Foundation of Korea.)",
  "License": "GPL (>= 3)",
  "URL": "https://github.com/zeemkr/ncpen",
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  "Maintainer": "Dongshin Kim <dongshin.kim@outlook.com>",
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    "coef.ncpen",
    "control.ncpen",
    "cv.ncpen",
    "cv.ncpen.reg",
    "excluded",
    "fold.cv.ncpen",
    "gic.ncpen",
    "interact.data",
    "make.ncpen.data",
    "native_cpp_ncpen_fun_",
    "native_cpp_nr_fun_",
    "native_cpp_obj_fun_",
    "native_cpp_obj_grad_fun_",
    "native_cpp_obj_hess_fun_",
    "native_cpp_p_ncpen_fun_",
    "native_cpp_pen_fun_",
    "native_cpp_pen_grad_fun_",
    "native_cpp_qlasso_fun_",
    "native_cpp_set_dev_mode_",
    "ncpen",
    "ncpen.reg",
    "plot.cv.ncpen",
    "plot.ncpen",
    "power.data",
    "predict.ncpen",
    "sam.gen.ncpen",
    "same.base",
    "to.indicators",
    "to.ncpen.x.mat"
  ],
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      "page": "coef.cv.ncpen",
      "title": "coef.cv.ncpen: extracts the optimal coefficients from 'cv.ncpen'.",
      "topics": [
        "coef.cv.ncpen"
      ]
    },
    {
      "page": "coef.ncpen",
      "title": "coef.ncpen: extract the coefficients from an 'ncpen' object",
      "topics": [
        "coef.ncpen"
      ]
    },
    {
      "page": "control.ncpen",
      "title": "control.ncpen: do preliminary works for 'ncpen'.",
      "topics": [
        "control.ncpen"
      ]
    },
    {
      "page": "cv.ncpen",
      "title": "cv.ncpen: cross validation for 'ncpen'",
      "topics": [
        "cv.ncpen"
      ]
    },
    {
      "page": "cv.ncpen.reg",
      "title": "cv.ncpen: cross validation for 'ncpen'",
      "topics": [
        "cv.ncpen.reg"
      ]
    },
    {
      "page": "excluded",
      "title": "Check whether a pair should be excluded from interactions.",
      "topics": [
        "excluded"
      ]
    },
    {
      "page": "fold.cv.ncpen",
      "title": "fold.cv.ncpen: extracts fold ids for 'cv.ncpen'.",
      "topics": [
        "fold.cv.ncpen"
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    },
    {
      "page": "gic.ncpen",
      "title": "gic.ncpen: compute the generalized information criterion (GIC) for the selection of lambda",
      "topics": [
        "gic.ncpen"
      ]
    },
    {
      "page": "interact.data",
      "title": "Construct Interaction Matrix",
      "topics": [
        "interact.data"
      ]
    },
    {
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      "title": "Create ncpen Data Structure Using a Formula",
      "topics": [
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    },
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      "page": "native_cpp_ncpen_fun_",
      "title": "Native ncpen function.",
      "topics": [
        "native_cpp_ncpen_fun_"
      ]
    },
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      "page": "native_cpp_nr_fun_",
      "title": "N/A.",
      "topics": [
        "native_cpp_nr_fun_"
      ]
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      "title": "Native object function.",
      "topics": [
        "native_cpp_obj_fun_"
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    },
    {
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      "topics": [
        "native_cpp_obj_grad_fun_"
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    },
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      "topics": [
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    },
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      "topics": [
        "native_cpp_pen_grad_fun_"
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    },
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      "title": "N/A.",
      "topics": [
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      "title": "ncpen: nonconvex penalized estimation",
      "topics": [
        "ncpen"
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