MOFAT
1.0Maximum One-Factor-at-a-Time Designs
Overview
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.
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- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Code & Tests
- Cyclomatic complexity
- 8.0 median / 13 max
- Documented parameters
- 100%
Test coverage
Line coverage
–
Expression
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Tests / Examples
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Functions
2 2 exported
Complexity
8 avg / 13 max
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2 nodes / 0 edges
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People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2022-10-29
- Total releases
- 1 / 4 yrs
- License
- GPL (>= 2) OSI
- Download size
- 4.2 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet