Maximum One-Factor-at-a-Time Designs R package MOFAT version 1.0 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>. Software Imports: SLHD, stats V. Roshan Joseph <roshan@gatech.edu> Comprehensive R Archive Network (CRAN) Qian Xiao [aut], V. Roshan Joseph [aut, cre] GPL (>= 2) 2022-10-29 application/tgz https://CRAN.R-project.org/package=MOFAT doi:10.32614/CRAN.package.MOFAT