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  "Authors@R": "c(person(given = \"Wei\",\nfamily = \"Liu\",\nrole = c(\"aut\", \"cre\"),\nemail = \"LiuWeideng@gmail.com\"),\nperson(given = \"Huazhen\",\nfamily = \"Lin\",\nrole = \"aut\"),\nperson(given = \"Shurong\",\nfamily = \"Zheng\",\nrole = \"aut\"),\nperson(given = \"Jin\",\nfamily = \"Liu\",\nrole = \"aut\"),\nperson(given = \"Jinyu\",\nfamily = \"Nie\",\nrole = \"aut\"))",
  "Author": "Wei Liu [aut, cre], Huazhen Lin [aut], Shurong Zheng [aut], Jin\nLiu [aut], Jinyu Nie [aut]",
  "Maintainer": "Wei Liu <LiuWeideng@gmail.com>",
  "Description": "Generalized factor model is implemented for ultra-high\ndimensional data with mixed-type variables. Two algorithms,\nvariational EM and alternate maximization, are designed to\nimplement the generalized factor model, respectively. The\nfactor matrix and loading matrix together with the number of\nfactors can be well estimated. This model can be employed in\nsocial and behavioral sciences, economy and finance, and\ngenomics, to extract interpretable nonlinear factors. More\ndetails can be referred to Wei Liu, Huazhen Lin, Shurong Zheng\nand Jin Liu. (2023) <doi:10.1080/01621459.2021.1999818>.",
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