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  "Package": "TriDimRegression",
  "Title": "Bayesian Statistics for 2D/3D Transformations",
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  "Authors@R": "c(\nperson(given = \"Alexander (Sasha)\",\nfamily = \"Pastukhov\",\nrole = c(\"aut\", \"cre\"),\nemail =\"pastukhov.alexander@gmail.com\",\ncomment = c(ORCID = \"0000-0002-8738-8591\")),\nperson(given = \"Claus-Christian\",\nfamily = \"Carbon\",\nrole = c(\"aut\"),\nemail = \"ccc@experimental-psychology.com\",\ncomment = c(ORCID = \"0000-0002-3446-9347\")))",
  "Description": "Fits 2D and 3D geometric transformations via 'Stan'\nprobabilistic programming engine ( Stan Development Team (2021)\n<https://mc-stan.org>). Returns posterior distribution for\nindividual parameters of the fitted distribution. Allows for\ncomputation of LOO and WAIC information criteria (Vehtari A,\nGelman A, Gabry J (2017) <doi:10.1007/s11222-016-9696-4>) as\nwell as Bayesian R-squared (Gelman A, Goodrich B, Gabry J, and\nVehtari A (2018) <doi:10.1080/00031305.2018.1549100>).",
  "License": "GPL-3",
  "URL": "https://github.com/alexander-pastukhov/tridim-regression,\nhttps://alexander-pastukhov.github.io/tridim-regression/",
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  "Author": "Alexander (Sasha) Pastukhov [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-8738-8591>),\nClaus-Christian Carbon [aut] (ORCID:\n<https://orcid.org/0000-0002-3446-9347>)",
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    "tridimenisional-regression",
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      "date": "2023-09-13"
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    "check_normal_prior",
    "check_variables",
    "coef_summary",
    "fit_transformation",
    "fit_transformation_df",
    "get_beta_n",
    "is.tridim_transformation",
    "m2_affine",
    "m2_euclidean",
    "m2_projective",
    "m2_translation",
    "m3_affine",
    "m3_euclidean_x",
    "m3_euclidean_y",
    "m3_euclidean_z",
    "m3_projective",
    "m3_translation",
    "R2",
    "transformation_matrix",
    "variable_summary"
  ],
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      "title": "Carbon, C. C. (2013), data set #1",
      "object": "CarbonExample1Data",
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      ],
      "fields": [
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    {
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      "object": "CarbonExample3Data",
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      ],
      "fields": [
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        "indepV2"
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      "rows": 10,
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    },
    {
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      "title": "Eye gaze calibration data",
      "object": "EyegazeData",
      "class": [
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      ],
      "fields": [
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      "rows": 365,
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    {
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      "table": true,
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  "_help": [
    {
      "page": "TriDimRegression-package",
      "title": "The 'TriDimRegression' package.",
      "topics": [
        "TriDimRegression-package",
        "TriDimRegression"
      ]
    },
    {
      "page": "CarbonExample1Data",
      "title": "Carbon, C. C. (2013), data set #1",
      "topics": [
        "CarbonExample1Data"
      ]
    },
    {
      "page": "CarbonExample2Data",
      "title": "Carbon, C. C. (2013), data set #2",
      "topics": [
        "CarbonExample2Data"
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    },
    {
      "page": "CarbonExample3Data",
      "title": "Carbon, C. C. (2013), data set #3",
      "topics": [
        "CarbonExample3Data"
      ]
    },
    {
      "page": "coef.tridim_transformation",
      "title": "Posterior distributions for transformation coefficients in full or summarized form.",
      "topics": [
        "coef.tridim_transformation"
      ]
    },
    {
      "page": "EyegazeData",
      "title": "Eye gaze calibration data",
      "topics": [
        "EyegazeData"
      ]
    },
    {
      "page": "Face3D_M010",
      "title": "Face landmarks, male, #010",
      "topics": [
        "Face3D_M010"
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    },
    {
      "page": "Face3D_M101",
      "title": "Face landmarks, male, #101",
      "topics": [
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    {
      "page": "Face3D_M244",
      "title": "Face landmarks, male, #244",
      "topics": [
        "Face3D_M244"
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    },
    {
      "page": "Face3D_M92",
      "title": "Face landmarks, male, #092",
      "topics": [
        "Face3D_M92"
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    },
    {
      "page": "Face3D_W070",
      "title": "Face landmarks, female, #070",
      "topics": [
        "Face3D_W070"
      ]
    },
    {
      "page": "Face3D_W097",
      "title": "Face landmarks, female, #097",
      "topics": [
        "Face3D_W097"
      ]
    },
    {
      "page": "Face3D_W182",
      "title": "Face landmarks, female, #182",
      "topics": [
        "Face3D_W182"
      ]
    },
    {
      "page": "Face3D_W243",
      "title": "Face landmarks, female, #243",
      "topics": [
        "Face3D_W243"
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    },
    {
      "page": "fit_transformation",
      "title": "Fitting Bidimensional or Tridimensional Regression / Geometric Transformation Models via Formula.",
      "topics": [
        "fit_transformation",
        "fit_transformation.formula"
      ]
    },
    {
      "page": "fit_transformation_df",
      "title": "Fitting Bidimensional or Tridimensional Regression / Geometric Transformation Models via Two Tables.",
      "topics": [
        "fit_transformation_df"
      ]
    },
    {
      "page": "FriedmanKohlerData1",
      "title": "Friedman & Kohler (2003), data set #1",
      "topics": [
        "FriedmanKohlerData1"
      ]
    },
    {
      "page": "FriedmanKohlerData2",
      "title": "Friedman & Kohler (2003), data set #2",
      "topics": [
        "FriedmanKohlerData2"
      ]
    },
    {
      "page": "is.tridim_transformation",
      "title": "Checks if argument is a 'tridim_transformation' object",
      "topics": [
        "is.tridim_transformation"
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    },
    {
      "page": "loo.tridim_transformation",
      "title": "Computes an efficient approximate leave-one-out cross-validation via loo library. It can be used for a model comparison via loo::loo_compare() function.",
      "topics": [
        "loo.tridim_transformation"
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    },
    {
      "page": "NakayaData",
      "title": "Nakaya (1997)",
      "topics": [
        "NakayaData"
      ]
    },
    {
      "page": "plot.tridim_transformation",
      "title": "Posterior interval plots for key parameters. Uses bayesplot::mcmc_intervals.",
      "topics": [
        "plot.tridim_transformation"
      ]
    },
    {
      "page": "predict.tridim_transformation",
      "title": "Computes posterior samples for the posterior predictive distribution.",
      "topics": [
        "predict.tridim_transformation"
      ]
    },
    {
      "page": "print.tridim_transformation",
      "title": "Prints out tridim_transformation object",
      "topics": [
        "print.tridim_transformation"
      ]
    },
    {
      "page": "R2",
      "title": "Computes R-squared using Bayesian R-squared approach. For detail refer to: Andrew Gelman, Ben Goodrich, Jonah Gabry, and Aki Vehtari (2018). R-squared for Bayesian regression models. The American Statistician, doi:10.1080/00031305.2018.1549100.",
      "topics": [
        "R2",
        "R2.tridim_transformation"
      ]
    },
    {
      "page": "summary.tridim_transformation",
      "title": "Summary for a tridim_transformation object",
      "topics": [
        "summary.tridim_transformation"
      ]
    },
    {
      "page": "tridim_transformation-class",
      "title": "Class 'tridim_transformation'.",
      "topics": [
        "tridim_transformation",
        "tridim_transformation-class"
      ]
    },
    {
      "page": "waic.tridim_transformation",
      "title": "Computes widely applicable information criterion (WAIC).",
      "topics": [
        "waic.tridim_transformation"
      ]
    }
  ],
  "_readme": "https://github.com/alexander-pastukhov/tridim-regression/raw/HEAD/README.md",
  "_rundeps": [
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