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Identifying important factors from a large number of potentially important factors of a highly nonlinear and computationally expensive black box model is a difficult problem. Xiao, Joseph, and Ray (2022) <doi:10.1080/00401706.2022.2141897> proposed Maximum One-Factor-at-a-Time (MOFAT) designs for doing this. A MOFAT design can be viewed as an improvement to the random one-factor-at-a-time (OFAT) design proposed by Morris (1991) <doi:10.1080/00401706.1991.10484804>. The improvement is achieved by exploiting the connection between Morris screening designs and Monte Carlo-based Sobol' designs, and optimizing the design using a space-filling criterion. This work is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921646>.
| Version: | 1.0 |
| Imports: | SLHD, stats |
| Published: | 2022-10-29 |
| Author: | Qian Xiao [aut], V. Roshan Joseph [aut, cre] |
| Maintainer: | V. Roshan Joseph <roshan at gatech.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | MOFAT results |
| Reference manual: | MOFAT.pdf |
| Package source: | MOFAT_1.0.tar.gz |
| Windows binaries: | r-devel: MOFAT_1.0.zip, r-release: MOFAT_1.0.zip, r-oldrel: MOFAT_1.0.zip |
| macOS binaries: | r-release (arm64): MOFAT_1.0.tgz, r-oldrel (arm64): MOFAT_1.0.tgz, r-release (x86_64): MOFAT_1.0.tgz, r-oldrel (x86_64): MOFAT_1.0.tgz |
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