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  "Title": "Taxicab Correspondence Analysis",
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  "Author": "Jacques Allard and Vartan Choulakian",
  "Maintainer": "Jacques Allard <jacques.allard@gmail.com>",
  "Description": "Computation and visualization of Taxicab Correspondence\nAnalysis, Choulakian (2006) <doi:10.1007/s11336-004-1231-4>.\nClassical correspondence analysis (CA) is a statistical method\nto analyse 2-dimensional tables of positive numbers and is\ntypically applied to contingency tables (Benzecri, J.-P.\n(1973). L'Analyse des Donnees. Volume II. L'Analyse des\nCorrespondances. Paris, France: Dunod).  Classical CA is based\non the Euclidean distance.  Taxicab CA is like classical CA but\nis based on the Taxicab or Manhattan distance.  For some\ntables, Taxicab CA gives more informative results than\nclassical CA.",
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  "Date/Publication": "2019-12-11 13:30:03 UTC",
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      "date": "2018-07-18"
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    "ComputeLambda",
    "CreateAllBinaries",
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    "ListToObjects",
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    "saveTCA",
    "SearchCrissCross",
    "SearchExhaustive",
    "SearchGeneticAlgoritm",
    "tca"
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      "title": "Counts of archeological objects",
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      "class": [
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      ],
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        "M01",
        "M02",
        "M03",
        "M04",
        "M05",
        "M06",
        "M08",
        "M09",
        "M10",
        "M16",
        "M18",
        "M20",
        "M04A",
        "M11",
        "M12",
        "M13",
        "M14",
        "M15",
        "M19"
      ],
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      "table": true,
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    },
    {
      "name": "rodent",
      "title": "Rodent species abundance",
      "object": "rodent",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Rt.rattus",
        "Mus.musculus",
        "Pm.californicus",
        "Pm.eremicus",
        "Rs.megalotis",
        "N.fuscipes",
        "N.lepida",
        "Pg.fallax",
        "M.californicus"
      ],
      "rows": 28,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "CombineCollinearRowsCols",
      "title": "Removes rows and columns of zeros and optionnally, row or column duplicates",
      "topics": [
        "CombineCollinearRowsCols"
      ]
    },
    {
      "page": "ComputeLambda",
      "title": "L1 norm of a projection",
      "topics": [
        "ComputeLambda"
      ]
    },
    {
      "page": "CreateAllBinaries",
      "title": "Create all 2^n n-tuples of ab[1] and ab[2]",
      "topics": [
        "CreateAllBinaries"
      ]
    },
    {
      "page": "JitterPosition",
      "title": "Moves points that are close together",
      "topics": [
        "JitterPosition"
      ]
    },
    {
      "page": "ListToObjects",
      "title": "Extract objects from a list",
      "topics": [
        "ListToObjects"
      ]
    },
    {
      "page": "milazzese",
      "title": "Counts of archeological objects",
      "topics": [
        "milazzese"
      ]
    },
    {
      "page": "plot.tca",
      "title": "Creates a symmetric plot from a tca-class object",
      "topics": [
        "plot.tca"
      ]
    },
    {
      "page": "print.tca",
      "title": "Print result of Taxicab Analysis in easily readable format",
      "topics": [
        "print.tca"
      ]
    },
    {
      "page": "RemoveRowsColumns0sAndDuplicates",
      "title": "Removes rows and columns of zeros and optionnally, row or column duplicates",
      "topics": [
        "RemoveRowsColumns0sAndDuplicates"
      ]
    },
    {
      "page": "rodent",
      "title": "Rodent species abundance",
      "topics": [
        "rodent"
      ]
    },
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      "page": "saveTCA",
      "title": "Save tca results to a folder",
      "topics": [
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      ]
    },
    {
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      "topics": [
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      ]
    },
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      "topics": [
        "SearchExhaustive"
      ]
    },
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      "title": "Search a taxicab principal component using the genetic algorithm",
      "topics": [
        "SearchGeneticAlgoritm"
      ]
    },
    {
      "page": "summary.tca",
      "title": "Summary of the Taxicab analysis",
      "topics": [
        "summary.tca"
      ]
    },
    {
      "page": "tca",
      "title": "Taxicab Correspondance analysis",
      "topics": [
        "tca"
      ]
    }
  ],
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