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  "Description": "Penalized regression for generalized linear models for\nmeasurement error problems (aka. errors-in-variables). The\npackage contains a version of the lasso (L1-penalization) which\ncorrects for measurement error (Sorensen et al. (2015)\n<doi:10.5705/ss.2013.180>). It also contains an implementation\nof the Generalized Matrix Uncertainty Selector, which is a\nversion the (Generalized) Dantzig Selector for the case of\nmeasurement error (Sorensen et al. (2018)\n<doi:10.1080/10618600.2018.1425626>).",
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      "title": "Extract Coefficients of a Generalized Dantzig Selector Object",
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      ]
    },
    {
      "page": "coef.gmu_lasso",
      "title": "Extract Coefficients of a GMU Lasso object",
      "topics": [
        "coef.gmu_lasso"
      ]
    },
    {
      "page": "coef.gmus",
      "title": "Extract Coefficients of a GMUS object",
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      "title": "Corrected Lasso",
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    },
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    },
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      ]
    },
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      "title": "Generalized Matrix Uncertainty Selector",
      "topics": [
        "gmus"
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    },
    {
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      "title": "Matrix Uncertainty Selector",
      "topics": [
        "mus"
      ]
    },
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      "title": "plot.corrected_lasso",
      "topics": [
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      "topics": [
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      "topics": [
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      ]
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      "title": "Plot the estimates returned by gmu_lasso",
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      "topics": [
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    },
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      "topics": [
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        "print.cv_gds"
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    },
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      "title": "Print a Generalized Dantzig Selector Object",
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    },
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      "title": "The hdme package: regression methods for high-dimensional data with measurement error",
      "author": "Oystein Sorensen",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Measurement Error in Regression",
        "Classical Measurement Error Problems",
        "Measurement Error in High-Dimensional Regression Problems",
        "The lasso",
        "The Dantzig Selector",
        "Generalized Dantzig Selector",
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        "Corrected Lasso for Linear Regression",
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        "Logistic Regression",
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