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  "Title": "Maximum Likelihood Conjoint Measurement",
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  "Date": "2026-02-15",
  "Authors@R": "c(person(\"Ken\", \"Knoblauch\", role = \"aut\", email = \"ken.knoblauch@inserm.fr\"), \nperson(c(\"Laurence\", \"T.\"), \"Maloney\", role = \"aut\"),\nperson(\"Guillermo\", \"Aguilar\", role=c(\"aut\", \"cre\"), email=\"guillermo.aguilar@mail.tu-berlin.de\"))",
  "Description": "Conjoint measurement is a psychophysical procedure in\nwhich stimulus pairs are presented that vary along 2 or more\ndimensions and the observer is required to compare the stimuli\nalong one of them.  This package contains functions to estimate\nthe contribution of the n scales to the judgment by a maximum\nlikelihood method under several hypotheses of how the\nperceptual dimensions interact. Reference: Knoblauch & Maloney\n(2012) \"Modeling Psychophysical Data in R\".\n<doi:10.1007/978-1-4614-4475-6>.",
  "License": "GPL (>= 2)",
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  "Author": "Ken Knoblauch [aut], Laurence T. Maloney [aut], Guillermo\nAguilar [aut, cre]",
  "Maintainer": "Guillermo Aguilar <guillermo.aguilar@mail.tu-berlin.de>",
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    "boot.mlcm",
    "make.wide",
    "make.wide.full",
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    "mlcm.default",
    "mlcm.formula"
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      "name": "BumpyGlossy",
      "title": "Conjoint Measurement Data for Bumpiness and Glossiness",
      "object": "BumpyGlossy",
      "class": [
        "mlcm.df",
        "data.frame"
      ],
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        "Resp",
        "G1",
        "G2",
        "B1",
        "B2"
      ],
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      "table": true,
      "tojson": true
    },
    {
      "name": "GlossyBumpy",
      "title": "Conjoint Measurement Data for Bumpiness and Glossiness",
      "object": "GlossyBumpy",
      "class": [
        "mlcm.df",
        "data.frame"
      ],
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        "G1",
        "G2",
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        "B2"
      ],
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      "table": true,
      "tojson": true
    },
    {
      "name": "Texture",
      "title": "Three-way Conjoint Measurement Data for Texture Regularity.",
      "object": "Texture",
      "class": [
        "mlcm.df",
        "data.frame"
      ],
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        "Resp",
        "S1",
        "S2",
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        "Z2",
        "J1",
        "J2"
      ],
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      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "MLCM-package",
      "title": "Maximum Likelihood Conjoint Measurement",
      "topics": [
        "MLCM-package",
        "MLCM"
      ]
    },
    {
      "page": "anova.mlcm",
      "title": "Analysis of Deviance for Maximum Likelihood Conjoint Measurement Model Fits",
      "topics": [
        "anova.mlcm"
      ]
    },
    {
      "page": "as.mlcm.df",
      "title": "Coerce data frame to mlcm.df",
      "topics": [
        "as.mlcm.df"
      ]
    },
    {
      "page": "binom.diagnostics",
      "title": "Diagnostics for Binary GLM",
      "topics": [
        "binom.diagnostics",
        "plot.mlcm.diag"
      ]
    },
    {
      "page": "boot.mlcm",
      "title": "Resampling of an Estimated Conjoint Measurement Scale",
      "topics": [
        "boot.mlcm"
      ]
    },
    {
      "page": "BumpyGlossy",
      "title": "Conjoint Measurement Data for Bumpiness and Glossiness",
      "topics": [
        "BumpyGlossy",
        "GlossyBumpy"
      ]
    },
    {
      "page": "fitted.mlcm",
      "title": "Fitted Responses for a Conjoint Measurement Scale",
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        "fitted.mlcm"
      ]
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    {
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      "title": "Extract Log-Likelihood from mlcm Object",
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        "logLik.mlcm"
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      "title": "Create data frame for Fitting Conjoint Measurment Models by glm",
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        "make.wide.full"
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    {
      "page": "mlcm",
      "title": "Fit Conjoint Measurement Models by Maximum Likelihood",
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        "mlcm.default",
        "mlcm.formula",
        "print.mlcm"
      ]
    },
    {
      "page": "plot.mlcm",
      "title": "Plot an mlcm Object",
      "topics": [
        "lines.mlcm",
        "plot.mlcm",
        "points.mlcm"
      ]
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    {
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      "title": "Create Conjoint Proportion Plot from mlcm.df Object",
      "topics": [
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      "title": "Predict Method for MLCM Objects",
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        "summary.mlcm"
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      "title": "Three-way Conjoint Measurement Data for Texture Regularity.",
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