{
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  "Package": "FactorCopulaModel",
  "Title": "Factor Copula Models",
  "Version": "0.1.1",
  "Authors@R": "c(\nperson(\"Harry\", \"Joe\", role = \"aut\"),\nperson(\"Pavel\", \"Krupskii\", email = \"pavel.krupskiy@unimelb.edu.au\", role = c(\"aut\",\"cre\")),\nperson(\"Xinyao\", \"Fan\", role = \"aut\"),\nperson(\"Allan\", \"Macleod\", role = \"cph\"),\nperson(\"Robert\", \"Gentleman\", role = \"cph\"),\nperson(\"Ross\", \"Ihaka\", role = \"cph\"))",
  "Description": "Inference methods for factor copula models for continuous\ndata in Krupskii and Joe (2013)\n<doi:10.1016/j.jmva.2013.05.001>, Krupskii and Joe (2015)\n<doi:10.1016/j.jmva.2014.11.002>, Fan and Joe (2024)\n<doi:10.1016/j.jmva.2023.105263>, one factor truncated vine\nmodels in Joe (2018) <doi:10.1002/cjs.11481>, and Gaussian\noblique factor models. Functions for computing tail-weighted\ndependence measures in Lee, Joe and Krupskii (2018)\n<doi:10.1080/10485252.2017.1407414> and estimating tail\ndependence parameter.",
  "License": "GPL-3",
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  "Packaged": {
    "Date": "2026-06-04 08:24:06 UTC",
    "User": "root"
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  "Author": "Harry Joe [aut], Pavel Krupskii [aut, cre], Xinyao Fan [aut],\nAllan Macleod [cph], Robert Gentleman [cph], Ross Ihaka [cph]",
  "Maintainer": "Pavel Krupskii <pavel.krupskiy@unimelb.edu.au>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2025-11-06 08:44:12 UTC",
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    "bifactor_fa",
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    "bifactor2cor",
    "bifactor2cor_v2",
    "bifactorcop_nllk",
    "bifactorEstWithProxy",
    "bifactorScore",
    "bvn_cpar2tau",
    "bvnSemiCor",
    "corDis",
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    "frank_beta2cpar",
    "frank_rhoS2cpar",
    "gauss1f1t",
    "gaussLegendre",
    "gumbel_beta2cpar",
    "gumbel_rhoS2cpar",
    "isPosDef",
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    "latentUpdate1factor1",
    "latentUpdateBifactor",
    "ml1factor",
    "ml1factor_f90",
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    "mvtBifact",
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    "mvtPfact",
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    "nestfactorcop_nllk",
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    "onefactorcop_nllk",
    "onefactorEstWithProxy",
    "pcor2load",
    "pfactor_fa",
    "pfactor_nllk",
    "posDefHessMin",
    "posDefHessMinb",
    "qcondbvtcop",
    "qcondFrank",
    "r1factor",
    "rbifactor",
    "rhoS",
    "rmvn",
    "rmvt",
    "rnestfactor",
    "semiCor",
    "semiCorTable",
    "tailDep",
    "uscore",
    "zetaDep",
    "zetaDepC",
    "zetaPlot"
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  "_datasets": [
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      "title": "GARCH-filtered log returns for Dow Jones stocks 2014-2016",
      "object": "DJ20142016gf",
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      "fields": [],
      "table": false,
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      "table": false,
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      "object": "DJ20142016gf",
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      "fields": [],
      "table": false,
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      "object": "rainstorm",
      "class": [
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      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
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      "page": "bb1_cpar2td",
      "title": "BB1 copula parameter (theta,delta) to tail dependence parameters",
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      ]
    },
    {
      "page": "bb1_tau2eqtd",
      "title": "BB1, given 0<tau<1, find theta and delta with lower tail dependence equal upper tail dependence",
      "topics": [
        "bb1_tau2eqtd"
      ]
    },
    {
      "page": "bb1_td2cpar",
      "title": "BB1 tail dependence parameters to copula parameter (theta,delta)",
      "topics": [
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    },
    {
      "page": "bifactor_fa",
      "title": "Gaussian bi-factor structure correlation matrix",
      "topics": [
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      "title": "log-likelihood Gaussian bi-factor structure correlation matrix",
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      ]
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    {
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      "topics": [
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    },
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      "page": "bifactorcop_nllk",
      "title": "negative log-likelihood of bi-factor structured factor copula and derivatives computed in f90 for input to posDefHessMinb",
      "topics": [
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      "title": "Sequential parameter estimation for bi-factor copula with estimated latent variables using VineCopula::BiCopSelect",
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    {
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      "title": "Proxies for bi-factor copula model based on Gaussian bi-factor score",
      "topics": [
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    },
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      "page": "bvn_cpar2tau",
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      "topics": [
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    },
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      "page": "bvnSemiCor",
      "title": "Semi-correlation for bivariate normal/Gaussian distribution",
      "topics": [
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      ]
    },
    {
      "page": "corDis",
      "title": "Discrepancy of model-based and observed correlation matrices based on Gaussian log-likelihood",
      "topics": [
        "corDis"
      ]
    },
    {
      "page": "corvec2mat",
      "title": "Convert from correlations in vector form to a correlation matrix",
      "topics": [
        "corvec2mat"
      ]
    },
    {
      "page": "cparBounds",
      "title": "lower and upper bounds for copula parameters (1-parameter, 2-parameter families)",
      "topics": [
        "cparBounds"
      ]
    },
    {
      "page": "d1factcop",
      "title": "Integrand for 1-factor copula with 1-parameter bivariate linking copula families; or for m-parameter bivariate linking copulas",
      "topics": [
        "d1factcop"
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    },
    {
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      "title": "log returns and GARCH-filtered log returns for some Euro markets 2007",
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        "euro07gf",
        "euro07names"
      ]
    },
    {
      "page": "factor1trvine_nllk",
      "title": "negative log-likelihood with gradient and Hessian computed in f90 for copula from 1-factor/1-truncated vine (tree for residual dependence conditional on a latent variable); models included are BB1 for latent with Frank or Gaussian(bvncop) for truncated vine residual dependence",
      "topics": [
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    },
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      "page": "frank_beta2cpar",
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      "topics": [
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      ]
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      "page": "frank_rhoS2cpar",
      "title": "Frank: Spearman rho to copula parameter",
      "topics": [
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      "title": "Compute correlation matrix according to 1-factor + 1-truncated vine (residual dependence) model",
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    },
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      "title": "R interface for Gauss-Legendre quadrature",
      "topics": [
        "gaussLegendre"
      ]
    },
    {
      "page": "gumbel_beta2cpar",
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      ]
    },
    {
      "page": "gumbel_rhoS2cpar",
      "title": "Gumbel: Spearman rho to copula parameter",
      "topics": [
        "gumbel_rhoS2cpar"
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    },
    {
      "page": "isPosDef",
      "title": "Check if a square symmetric matrix is positive definite",
      "topics": [
        "isPosDef"
      ]
    },
    {
      "page": "latentUpdate1factor",
      "title": "Compute new proxies for 1-factor copula based on the mean of observations",
      "topics": [
        "latentUpdate1factor"
      ]
    },
    {
      "page": "latentUpdate1factor1",
      "title": "Compute new proxies for 1-factor copula based on the mean of observations",
      "topics": [
        "latentUpdate1factor1"
      ]
    },
    {
      "page": "latentUpdateBifactor",
      "title": "Conditional expectation proxies for bi-factor copula models with linking copulas in different copula families",
      "topics": [
        "latentUpdateBifactor"
      ]
    },
    {
      "page": "ml1factor",
      "title": "max likelihood (min negative log-likelihood) for 1-factor copula model",
      "topics": [
        "ml1factor"
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    },
    {
      "page": "ml1factor_f90",
      "title": "min negative log-likelihood for 1-factor copula with nlm()",
      "topics": [
        "ml1factor_f90"
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    },
    {
      "page": "ml1factor_v2",
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      "page": "mvtBifact",
      "title": "MLE for multivariate normal/t with a bi-factor or nested factor correlation structure",
      "topics": [
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      "page": "mvtBifact_nllk",
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      "topics": [
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      "title": "MLE in a MVt model with a p-factor correlation structure",
      "topics": [
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    {
      "page": "mvtPfact_nllk",
      "title": "negative log-likelihood for the p-factor Gaussian/t model",
      "topics": [
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    },
    {
      "page": "nestfactorcop_nllk",
      "title": "negative log-likelihoods of nested factor structured factor copula and derivatives computed in f90 for input to posDefHessMinb",
      "topics": [
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      "title": "Rank-based normal scores transform",
      "topics": [
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    },
    {
      "page": "oblique_fa",
      "title": "Gaussian oblique factor structure correlation matrix",
      "topics": [
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      "page": "oblique_grad_fa",
      "title": "Gaussian oblique factor structure correlation matrix",
      "topics": [
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    {
      "page": "oblique_grad_nllk",
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      "title": "log-likelihood Gaussian oblique factor structure correlation matrix",
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      "title": "oblique factor correlation structure for d variables and m groups",
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      "title": "negative log-likelihood of 1-factor copula for input to posDefHessMin and posDefHessMinb",
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      "page": "onefactorEstWithProxy",
      "title": "Parameter estimation for 1-factor copula with estimated latent variables using VineCopula::BiCopSeelct",
      "topics": [
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      "page": "pcor2load",
      "title": "Partial correlation representation to loadings for p-factor",
      "topics": [
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    {
      "page": "pfactor_fa",
      "title": "Gaussian p-factor structure correlation matrix",
      "topics": [
        "pfactor_fa"
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    {
      "page": "pfactor_nllk",
      "title": "log-likelihood Gaussian p-factor structure correlation matrix",
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      "page": "posDefHessMin",
      "title": "Minimization with modified Newton-Raphson iterations, Hessian is modified to be positive definite at each step.  Algorithm and code produced by Pavel Krupskii (2013) see PhD thesis Krupskii (2014), UBC and Section 6.2 of # Joe (2014) Dependence Models with Copulas. Chapman&Hall/CRC",
      "topics": [
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      "page": "posDefHessMinb",
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      "topics": [
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      "page": "qcondbvtcop",
      "title": "C_[2|1]^[-1](p|u) for bivariate Student t copula",
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      "page": "qcondFrank",
      "title": "C_[2|1]^[-1](p|u) for bivariate Frank copula",
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      "page": "r1factor",
      "title": "simulate from 1-factor copula model with different linking copula families",
      "topics": [
        "r1factor"
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      "page": "rainstorm",
      "title": "Precipitation by rainstorm at 28 stations",
      "topics": [
        "rainstorm"
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    },
    {
      "page": "rbifactor",
      "title": "simulate from bi-factor copula model",
      "topics": [
        "rbifactor"
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    {
      "page": "residDep",
      "title": "correlation matrix for 1-factor plus 1-truncated vine (for residual dependence)",
      "topics": [
        "residDep"
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      "page": "rhoS",
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      "topics": [
        "rhoS"
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      "page": "rmvn",
      "title": "Random multivariate normal (standard N(0,1) margins)",
      "topics": [
        "rmvn"
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    },
    {
      "page": "rmvt",
      "title": "Random multivariate t (standard t(nu) margins)",
      "topics": [
        "rmvt"
      ]
    },
    {
      "page": "rnestfactor",
      "title": "Simulate data from nested copula or Gaussian model",
      "topics": [
        "rnestfactor"
      ]
    },
    {
      "page": "RVtrunc2cor",
      "title": "compute correlation matrix from 2-truncated R-vine",
      "topics": [
        "RVtrunc2cor"
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      "page": "semiCor",
      "title": "Semi-correlations for two variables",
      "topics": [
        "semiCor"
      ]
    },
    {
      "page": "semiCorTable",
      "title": "Semi-correlation table for a multivariate data set",
      "topics": [
        "semiCorTable"
      ]
    },
    {
      "page": "tailDep",
      "title": "Tail dependence parameter estimation",
      "topics": [
        "tailDep"
      ]
    },
    {
      "page": "uscore",
      "title": "Rank-based uniform scores transform",
      "topics": [
        "uscore"
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    },
    {
      "page": "zetaDep",
      "title": "Empirical version of zeta(alpha) tail-weighted dependence measure",
      "topics": [
        "zetaDep"
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    },
    {
      "page": "zetaDepC",
      "title": "Upper Tail-weighted dependence measure zeta(C,alpha)",
      "topics": [
        "zetaDepC"
      ]
    },
    {
      "page": "zetaPlot",
      "title": "Plot zeta(alpha) against alpha",
      "topics": [
        "zetaPlot"
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    }
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