{
  "_id": "6a252ea24b233be19839566a",
  "Package": "LCAextend",
  "Type": "Package",
  "Title": "Latent Class Analysis (LCA) with Familial Dependence in Extended\nPedigrees",
  "Version": "1.3",
  "Date": "2018-07-05",
  "Author": "Arafat TAYEB <arafat.tayeb@ircm.qc.ca>, Alexandre BUREAU\n<alexandre.bureau@msp.ulaval.ca> and Aurelie Labbe\n<aurelie.labbe@mcgill.ca>",
  "Maintainer": "Alexandre BUREAU <alexandre.bureau@msp.ulaval.ca>",
  "Description": "Latent Class Analysis of phenotypic measurements in\npedigrees and model selection based on one of two methods:\nlikelihood-based cross-validation and Bayesian Information\nCriterion. Computation of individual and triplet child-parents\nweights in a pedigree is performed using an upward-downward\nalgorithm. The model takes into account the familial dependence\ndefined by the pedigree structure by considering that a class\nof a child depends on his parents classes via\ntriplet-transition probabilities of the classes. The package\nhandles the case where measurements are available on all\nsubjects and the case where measurements are available only on\nsymptomatic (i.e. affected) subjects. Distributions for\ndiscrete (or ordinal) and continuous data are currently\nimplemented. The package can deal with missing data.",
  "License": "GPL",
  "LazyLoad": "yes",
  "URL": "https://CRAN.R-project.org/package=LCAextend",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-07 08:37:45 UTC",
    "User": "root"
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2018-07-07 14:40:21 UTC",
  "RemoteUrl": "https://github.com/cran/LCAextend",
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    "author": "Alexandre BUREAU <alexandre.bureau@msp.ulaval.ca>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 1.3\n",
    "time": 1530974421
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  "_maintainer": {
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  "_owner": "cran",
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  "_userbio": {
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    "name": "cran",
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  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
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  "_realowner": "cran",
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    "attrib.dens",
    "dens.norm",
    "dens.prod.ordi",
    "downward",
    "downward.connect",
    "e.step",
    "init.norm",
    "init.ordi",
    "init.p.trans",
    "lca.model",
    "model.select",
    "n.param",
    "optim.const.ordi",
    "optim.diff.norm",
    "optim.equal.norm",
    "optim.gene.norm",
    "optim.indep.norm",
    "optim.noconst.ordi",
    "optim.probs",
    "p.compute",
    "p.post.child",
    "p.post.found",
    "upward",
    "upward.connect",
    "weight.famdep",
    "weight.nuc"
  ],
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      "name": "param.cont",
      "title": "parameters to be used for examples in the case of continuous measurements",
      "object": "param.cont",
      "file": "param.cont.rda",
      "class": [
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      ],
      "fields": [],
      "table": true,
      "tojson": true
    },
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      "name": "param.ordi",
      "title": "parameters to be used for examples in the case of discrete or ordinal measurements",
      "object": "param.ordi",
      "file": "param.ordi.rda",
      "class": [
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      "fields": [],
      "table": true,
      "tojson": true
    },
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      "title": "pedigrees with continuous data to be used for examples",
      "object": "ped.cont",
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        "array"
      ],
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    },
    {
      "name": "ped.ordi",
      "title": "pedigrees with discrete or ordinal data to be used for examples",
      "object": "ped.ordi",
      "file": "ped.ordi.rda",
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        "array"
      ],
      "fields": {},
      "rows": 722,
      "table": true,
      "tojson": true
    },
    {
      "name": "peel",
      "title": "peeling order of pedigrees and couples in pedigrees",
      "object": "peel",
      "file": "peel.rda",
      "class": [
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      ],
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      "table": true,
      "tojson": true
    },
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      "name": "probs",
      "title": "probabilities parameters to be used for examples",
      "object": "probs",
      "file": "probs.rda",
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      ],
      "fields": [],
      "table": false,
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  ],
  "_help": [
    {
      "page": "alpha.compute",
      "title": "computes cumulative logistic coefficients using probabilities",
      "topics": [
        "alpha.compute"
      ]
    },
    {
      "page": "attrib.dens",
      "title": "associates to a function of density parameter optimization an attribute to distinguish between ordinal and normal cases",
      "topics": [
        "attrib.dens"
      ]
    },
    {
      "page": "dens.norm",
      "title": "computes the multinormal density of a given continuous measurement vector for all classes",
      "topics": [
        "dens.norm"
      ]
    },
    {
      "page": "dens.prod.ordi",
      "title": "computes the probability of a given discrete measurement vector for all classes under a product of multinomial",
      "topics": [
        "dens.prod.ordi"
      ]
    },
    {
      "page": "downward",
      "title": "performs the downward step of the peeling algorithm and computes unnormalized triplet and individual weights",
      "topics": [
        "downward"
      ]
    },
    {
      "page": "downward.connect",
      "title": "performs a downward step for a connector",
      "topics": [
        "downward.connect"
      ]
    },
    {
      "page": "e.step",
      "title": "performs the E step of the EM algorithm for a single pedigree for both cases with and without familial dependence",
      "topics": [
        "e.step"
      ]
    },
    {
      "page": "init.norm",
      "title": "computes initial values for the EM algorithm in the case of continuous measurements",
      "topics": [
        "init.norm"
      ]
    },
    {
      "page": "init.ordi",
      "title": "computes the initial values for EM algorithm in the case of ordinal measurements",
      "topics": [
        "init.ordi"
      ]
    },
    {
      "page": "init.p.trans",
      "title": "initializes the transition probabilities",
      "topics": [
        "init.p.trans"
      ]
    },
    {
      "page": "lca.model",
      "title": "fits latent class models for phenotypic measurements in pedigrees with or without familial dependence using an Expectation-Maximization (EM) algorithm",
      "topics": [
        "lca.model"
      ]
    },
    {
      "page": "model.select",
      "title": "selects a latent class model for pedigree data",
      "topics": [
        "model.select"
      ]
    },
    {
      "page": "n.param",
      "title": "computes the number of parameters of a model",
      "topics": [
        "n.param"
      ]
    },
    {
      "page": "optim.const.ordi",
      "title": "performs the M step for the measurement distribution parameters in multinomial case, with an ordinal constraint on the parameters",
      "topics": [
        "optim.const.ordi"
      ]
    },
    {
      "page": "optim.diff.norm",
      "title": "performs the M step for measurement density parameters in multinormal case",
      "topics": [
        "optim.diff.norm"
      ]
    },
    {
      "page": "optim.equal.norm",
      "title": "performs the M step for measurement density parameters in multinormal case",
      "topics": [
        "optim.equal.norm"
      ]
    },
    {
      "page": "optim.gene.norm",
      "title": "performs the M step for measurement density parameters in multinormal case",
      "topics": [
        "optim.gene.norm"
      ]
    },
    {
      "page": "optim.indep.norm",
      "title": "performs the M step for measurement density parameters in multinormal case",
      "topics": [
        "optim.indep.norm"
      ]
    },
    {
      "page": "optim.noconst.ordi",
      "title": "performs the M step for the measurement distribution parameters in multinomial case without constraint on the parameters",
      "topics": [
        "optim.noconst.ordi"
      ]
    },
    {
      "page": "optim.probs",
      "title": "performs the M step of the EM algorithm for the probability parameters",
      "topics": [
        "optim.probs"
      ]
    },
    {
      "page": "p.compute",
      "title": "computes the probability vector using logistic coefficients",
      "topics": [
        "p.compute"
      ]
    },
    {
      "page": "p.post.child",
      "title": "computes the posterior probability of observations of a child",
      "topics": [
        "p.post.child"
      ]
    },
    {
      "page": "p.post.found",
      "title": "computes the posterior probability of observations of a founder",
      "topics": [
        "p.post.found"
      ]
    },
    {
      "page": "param.cont",
      "title": "parameters to be used for examples in the case of continuous measurements",
      "topics": [
        "param.cont"
      ]
    },
    {
      "page": "param.ordi",
      "title": "parameters to be used for examples in the case of discrete or ordinal measurements",
      "topics": [
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    },
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      "title": "pedigrees with continuous data to be used for examples",
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    },
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      "title": "pedigrees with discrete or ordinal data to be used for examples",
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    },
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    },
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      "title": "probabilities parameters to be used for examples",
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    },
    {
      "page": "upward",
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    },
    {
      "page": "upward.connect",
      "title": "performs the upward step for a connector",
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    },
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      "page": "weight.famdep",
      "title": "performs the computation of triplet and individual weights for a pedigree under familial dependence",
      "topics": [
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      ]
    },
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      "page": "weight.nuc",
      "title": "performs the computation of unnormalized triplet and individuals weights for a nuclear family in the pedigree",
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