Maximum One-Factor-at-a-Time Designs

Identifying important factors from a large number of potentially important factors in a highly nonlinear and computationally expensive black-box model is a difficult problem. Xiao, Joseph, and Ray (2022) proposed Maximum One-Factor-at-a-Time (MOFAT) designs for this purpose. A MOFAT design is a collection of one-factor-at-a-time (OFAT) designs constructed to achieve maximal screening capability. Song and Joseph (2026) extended the MOFAT framework to incorporate nominal and discrete numeric factors. The resulting designs are implemented in the mofatQQ function. Yu and Joseph (2026) proposed Space-Filling One-Factor-at-a-Time (SOFT) designs, which achieve optimal space-filling properties while retaining the screening capability. Therefore, SOFT designs are useful for both factor screening and surrogate modeling. The work on MOFAT is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646 < https://www.nsf.gov/awardsearch/show-award/?AWD_ID=1921646>, and the work on MOFATQQ and SOFT is supported by a U.S. NSF grant DMS-2310637.