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  "Title": "VITA, IG and PLSIM Simulation for Given Covariance and Marginals",
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  "Description": "Random sampling from distributions with user-specified\npopulation covariance matrix. Marginal information may be fully\nspecified, for which the package implements the VITA\n(VIne-To-Anything) algorithm Grønneberg and Foldnes (2017)\n<doi:10.1007/s11336-017-9569-6>.  See also Grønneberg, Foldnes\nand Marcoulides (2022) <doi:10.18637/jss.v102.i03>.\nAlternatively, marginal skewness and kurtosis may be specified,\nfor which the package implements the IG (independent generator)\nand PLSIM (piecewise linear) algorithms, see Foldnes and Olsson\n(2016) <doi:10.1080/00273171.2015.1133274> and Foldnes and\nGrønneberg (2021) <doi:10.1080/10705511.2021.1949323>,\nrespectively.",
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      "title": "Covsim: Simulating non-normal data with given covariance matrix",
      "author": "Njål Foldnes and Steffen Grønneberg",
      "engine": "knitr::rmarkdown",
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        "VITA",
        "Bivariate case",
        "Trivariate case",
        "SEM example in 20 dimensions",
        "Ordinal-categorical data",
        "We assume that the underlying correlation in acontinuous bivariate distribution with standard normal marginals is$\\rho=0.5$, and we discretize into three categories using thresholds$\\tau_1=0$ and $\\tau_2=1$. This means that we consider simulated dataof the form[X_i =\\left{  \\begin{matrix}1, & \\text{if } \\xi_i \\leq \\tau_1 \\2, & \\text{if } \\tau_1 < \\xi_i \\leq \\tau_2 \\3, & \\text{if } \\xi_i > \\tau_2 \\\\end{matrix}\\right.",
        "IG and Piecewise linear methods",
        "IG",
        "PLSIM",
        "References"
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      "modified": "2024-06-01 02:45:25",
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