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  "Title": "Visualization of Data Envelopment Analysis Problems",
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  "Description": "High-dimensional visualization methods for data\nenvelopment analysis (DEA), gathering in one place techniques\nthat have appeared in the literature but remained scattered and\nlargely unimplemented: cross-efficiency matrix unfolding, the\nPorembski network with lambda edges, principal component\nanalysis biplots, multidimensional-scaling colour-plots,\nself-organizing maps, the Costa bi-dimensional efficient\nfrontier, parallel coordinates, radar charts, panel-data\ntrajectory biplots, peer and reference networks, and a set of\ndescriptive plots. The package is built around a single\nvalidated dea_data() object and uses the 'Benchmarking' package\nas its DEA engine. The implemented methods draw on a body of\nliterature; representative references include Doyle and Green\n(1994) <doi:10.1057/jors.1994.84>, Porembski, Breitenstein and\nAlpar (2005) <doi:10.1007/s11123-005-1328-5> and Bana e Costa,\nSoares de Mello and Angulo Meza (2016)\n<doi:10.1016/j.ejor.2016.05.012>.",
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    "plot_io_scatter",
    "plot_io_som",
    "plot_io_som_components",
    "plot_panel_io_biplot",
    "standardize_weights"
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      "name": "chinese_cities",
      "title": "Inputs and outputs of 35 major Chinese cities",
      "object": "chinese_cities",
      "class": [
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      ],
      "fields": [
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        "industrial_labour_force",
        "working_funds",
        "investments",
        "gross_industrial_output",
        "profit_and_tax",
        "retail_sales"
      ],
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    {
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      "title": "Inputs and outputs of 22 Taiwanese commercial banks, 2009-2011",
      "object": "taiwanese_banks",
      "class": [
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        "demand_deposits",
        "short_term_loans",
        "long_term_loans"
      ],
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      "page": "as_dea_data",
      "title": "Coerce to a dea_data object",
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    {
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      "topics": [
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    },
    {
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      "title": "Compute DEA efficiency scores",
      "topics": [
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      ]
    },
    {
      "page": "compute_multiplier_weights",
      "title": "Compute DEA multiplier (weight) solutions",
      "topics": [
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    },
    {
      "page": "compute_som",
      "title": "Fit a self-organizing map to a DEA problem",
      "topics": [
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      ]
    },
    {
      "page": "cross-efficiency",
      "title": "Cross-efficiency and benevolent weights",
      "topics": [
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        "compute_cross_efficiency_weights",
        "cross-efficiency",
        "standardize_weights"
      ]
    },
    {
      "page": "dea_data",
      "title": "Create a DEA data object",
      "topics": [
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      ]
    },
    {
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      "title": "Heatmap of a cross-efficiency matrix",
      "topics": [
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      ]
    },
    {
      "page": "plot_cem_unfolding",
      "title": "Unfolding map of a cross-efficiency matrix",
      "topics": [
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    },
    {
      "page": "plot_cem_weights_heatmap",
      "title": "Heatmap of secondary-goal multiplier weights",
      "topics": [
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    },
    {
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      "title": "Efficiency scores of the DMUs",
      "topics": [
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      ]
    },
    {
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      "title": "Interactive 3-D scatter plot of a DEA problem",
      "topics": [
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      ]
    },
    {
      "page": "plot_io_costa_frontier",
      "title": "Costa bi-dimensional efficient frontier",
      "topics": [
        "plot_io_costa_frontier"
      ]
    },
    {
      "page": "plot_io_distributions",
      "title": "Distributions of inputs and outputs",
      "topics": [
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      ]
    },
    {
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      "title": "Counts of efficient and inefficient DMUs",
      "topics": [
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      ]
    },
    {
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    },
    {
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      "topics": [
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    },
    {
      "page": "plot_io_mds",
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    },
    {
      "page": "plot_io_parcoo",
      "title": "Parallel coordinates plot of a DEA problem",
      "topics": [
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    },
    {
      "page": "plot_io_pca_biplot",
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      "topics": [
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    {
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      "title": "DEA peer (target) network",
      "topics": [
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    {
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        "The data object",
        "Efficiency scores",
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