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  "Title": "Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares)\nEstimation",
  "Version": "1.3.2",
  "Date": "2023-08-20",
  "Author": "Mehmet Hakan Satman",
  "Maintainer": "Mehmet Hakan Satman <mhsatman@istanbul.edu.tr>",
  "Description": "Includes the ga.lts() function that estimates LTS (Least\nTrimmed Squares) parameters using genetic algorithms and\nC-steps. ga.lts() constructs a genetic algorithm to form a\nbasic subset and iterates C-steps as defined in Rousseeuw and\nvan-Driessen (2006) to calculate the cost value of the LTS\ncriterion. OLS (Ordinary Least Squares) regression is known to\nbe sensitive to outliers. A single outlying observation can\nchange the values of estimated parameters. LTS is a resistant\nestimator even the number of outliers is up to half of the\ndata. This package is for estimating the LTS parameters with\nlower bias and variance in a reasonable time. Version >=1.3\nincludes the function medmad for fast outlier detection in\nlinear regression.",
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  "LazyLoad": "yes",
  "Packaged": {
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    "User": "root"
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  "Repository": "https://jbytecode.r-universe.dev",
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
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  "_realowner": "jbytecode",
  "_cranurl": true,
  "_releases": [
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      "version": "1.0",
      "date": "2010-11-15"
    },
    {
      "version": "1.1",
      "date": "2011-02-05"
    },
    {
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      "date": "2012-01-07"
    },
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      "page": "galts-package",
      "title": "Genetic algorithms and C-steps based LTS (Least Trimmed Squares) estimation",
      "topics": [
        "galts-package",
        "galts"
      ]
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      "title": "Function for estimating the LTS (Least Trimmed Squares) regression parameters using genetic algorithms.",
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        "ga.lts"
      ]
    },
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      ]
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      ]
    }
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