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  "Title": "Maximum One-Factor-at-a-Time Designs",
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  "Authors@R": "c(\nperson(given = \"Qian\",\nfamily = \"Xiao\",\nrole = c(\"aut\"),\nemail = \"QIAN.XIAO@uga.edu\"),\nperson(given = \"V. Roshan\",\nfamily = \"Joseph\",\nrole = c(\"aut\", \"cre\"),\nemail = \"roshan@gatech.edu\"))",
  "Description": "Identifying important factors from a large number of\npotentially important factors of a highly nonlinear and\ncomputationally expensive black box model is a difficult\nproblem. Xiao, Joseph, and Ray (2022)\n<doi:10.1080/00401706.2022.2141897> proposed Maximum\nOne-Factor-at-a-Time (MOFAT) designs for doing this. A MOFAT\ndesign can be viewed as an improvement to the random\none-factor-at-a-time (OFAT) design proposed by Morris (1991)\n<doi:10.1080/00401706.1991.10484804>. The improvement is\nachieved by exploiting the connection between Morris screening\ndesigns and Monte Carlo-based Sobol' designs, and optimizing\nthe design using a space-filling criterion. This work is\nsupported by a U.S. National Science Foundation (NSF) grant\nCMMI-1921646\n<https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921646>.",
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