Package: MOFAT 2.0

MOFAT: 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) <doi:10.1080/00401706.2022.2141897> 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) <doi:10.48550/arXiv.2609.02087> extended the MOFAT framework to incorporate nominal and discrete numeric factors. The resulting designs are implemented in the mofatQQ function. Yu and Joseph (2026) <doi:10.48550/arXiv.2606.02533> 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.

Authors:Difan Song [aut, cre], Wei-Yang Yu [aut], Qian Xiao [aut], V. Roshan Joseph [aut]