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 <>.

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>
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:, r-release:, r-oldrel:
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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