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  "Package": "manymodelr",
  "Title": "Build and Tune Several Models",
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  "Authors@R": "person(given = \"Nelson\",\nfamily = \"Gonzabato\",\nemail = \"gonzabato@hotmail.com\",\nrole=c(\"aut\",\"cre\"))",
  "Description": "Frequently one needs a convenient way to build and tune\nseveral models in one go.The goal is to provide a number of\nmachine learning convenience functions. It provides the ability\nto build, tune and obtain predictions of several models in one\nfunction. The models are built using functions from 'caret'\nwith easier to read syntax. Kuhn(2014)\n<doi:10.48550/arXiv.1405.6974>.",
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  "Repository": "https://nelson-gon.r-universe.dev",
  "Date/Publication": "2025-08-22 19:33:47 UTC",
  "RemoteUrl": "https://github.com/nelson-gon/manymodelr",
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  "Author": "Nelson Gonzabato [aut, cre]",
  "Maintainer": "Nelson Gonzabato <gonzabato@hotmail.com>",
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    "extra/citation.html",
    "extra/citation.json",
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    "extra/contents.json",
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    "extra/NEWS.txt",
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    "extra/readme.md",
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      "date": "2019-03-04"
    },
    {
      "version": "0.2.1",
      "date": "2019-07-04"
    },
    {
      "version": "0.2.2",
      "date": "2019-07-08"
    },
    {
      "version": "0.2.3",
      "date": "2020-02-05"
    },
    {
      "version": "0.2.4",
      "date": "2020-02-12"
    },
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      "version": "0.3.1",
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    },
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    "agg_by_group",
    "drop_non_numeric",
    "extract_model_info",
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    "fit_models",
    "get_data_Stats",
    "get_exponent",
    "get_mode",
    "get_stats",
    "get_this",
    "get_var_corr",
    "get_var_corr_",
    "multi_model_1",
    "multi_model_2",
    "na_replace",
    "na_replace_grouped",
    "plot_corr",
    "report_model",
    "rowdiff",
    "select_col",
    "select_percentile"
  ],
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      "name": "yields",
      "title": "Plant yields",
      "object": "yields",
      "class": [
        "data.frame"
      ],
      "fields": [
        "normal",
        "height",
        "weight",
        "yield"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    }
  ],
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    {
      "page": "add_model_predictions",
      "title": "Add predictions to the data set. A dplyr compatible way to add predictions to a data set.",
      "topics": [
        "add_model_predictions"
      ]
    },
    {
      "page": "add_model_residuals",
      "title": "Add model residuals",
      "topics": [
        "add_model_residuals"
      ]
    },
    {
      "page": "agg_by_group",
      "title": "A convenient way to perform grouped operations",
      "topics": [
        "agg_by_group"
      ]
    },
    {
      "page": "drop_non_numeric",
      "title": "Drops non numeric columns from a data.frame object",
      "topics": [
        "drop_non_numeric"
      ]
    },
    {
      "page": "extract_model_info",
      "title": "Extract important model attributes",
      "topics": [
        "extract_model_info"
      ]
    },
    {
      "page": "fit_model",
      "title": "Fit and predict in a single function.",
      "topics": [
        "fit_model"
      ]
    },
    {
      "page": "fit_models",
      "title": "Fit several models with different response variables",
      "topics": [
        "fit_models"
      ]
    },
    {
      "page": "get_data_Stats",
      "title": "A pipe friendly way to get summary stats for exploratory data analysis",
      "topics": [
        "get_data_Stats",
        "get_stats"
      ]
    },
    {
      "page": "get_exponent",
      "title": "Get the exponent of any number or numbers",
      "topics": [
        "get_exponent"
      ]
    },
    {
      "page": "get_mode",
      "title": "A convenience function that returns the mode",
      "topics": [
        "get_mode"
      ]
    },
    {
      "page": "get_this",
      "title": "Helper function to easily access elements",
      "topics": [
        "get_this"
      ]
    },
    {
      "page": "get_var_corr",
      "title": "Get correlations between variables",
      "topics": [
        "get_var_corr"
      ]
    },
    {
      "page": "get_var_corr_",
      "title": "Get correlations for combinations",
      "topics": [
        "get_var_corr_"
      ]
    },
    {
      "page": "multi_model_1",
      "title": "Simultaneously train and predict on new data.",
      "topics": [
        "multi_model_1"
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    },
    {
      "page": "multi_model_2",
      "title": "Fit and predict in one function",
      "topics": [
        "multi_model_2"
      ]
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    }
  ]
}