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  "Title": "Two-Directional Simultaneous Inference for High-Dimensional\nModels",
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  "Date": "2023-01-26",
  "Authors@R": "c(person(given = \"Wei\",\nfamily = \"Liu\",\nrole = c(\"aut\", \"cre\"),\nemail = \"weiliu@smail.swufe.edu.cn\"),\nperson(given = \"Huazhen\",\nfamily = \"Lin\",\nrole = c(\"aut\")))",
  "Author": "Wei Liu [aut, cre], Huazhen Lin [aut]",
  "Maintainer": "Wei Liu <weiliu@smail.swufe.edu.cn>",
  "Description": "A general framework of two directional simultaneous\ninference is provided for high-dimensional as well as the fixed\ndimensional models with manifest variable or latent variable\nstructure, such as high-dimensional mean models, high-\ndimensional sparse regression models, and high-dimensional\nlatent factors models. It is making the simultaneous inference\non a set of parameters from two directions, one is testing\nwhether the estimated zero parameters indeed are zero and the\nother is testing whether there exists zero in the parameter set\nof non-zero. More details can be referred to Wei Liu, et al.\n(2023) <doi:10.1080/07350015.2023.2191672>.",
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  "Date/Publication": "2025-09-12 13:26:11 UTC",
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