MOFAT: Maximum One-Factor-at-a-Time Designs
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 |
| DOI: |
10.32614/CRAN.package.MOFAT |
| 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 |
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