Suggests: smoof, testthat, knitr, rmarkdown, irace
comment = "Original concept; organization; coding"),
comment = "Literature review; initial formalization of operators"),
Description: Modular implementation of MOEA/D approaches for multiobjective

Description: Modular implementation of Multiobjective Evolutionary Algorithms
based on Decomposition (MOEA/D) [Zhang and Li (2007),
<DOI:10.1109/TEVC.2007.892759>] for quick assembling and
testing of new algorithmic components, as well as easy
replication of published MOEA/D proposals.

Suggests: smoof, scatterplot3d, MASS, grDevices, irace, testthat,
knitr, rmarkdown, emoa, ggplot2, reshape2

replication of published MOEA/D proposals. The full framework is
documented in a paper published in the Journal of Statistical
knitr, rmarkdown, emoa, ggplot2, reshape2, pkgdown

Description: 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>.
Maintainer: V. Roshan Joseph <roshan@gatech.edu>

Title: Mojo Variants: Rare Variants analysis
Description: A package for analysis between rare variants and
quantitative traits by CMC (the combined multivariate and

Title: Multiple Objective Latin Hypercube Design
Description: Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) <doi:10.1016/0378-3758(94)00035-T>, Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) <doi:10.1198/Tech.2011.10087>, Joseph, V. R., Gul, E., and Ba, S. (2015) <doi:10.1093/biomet/asv002>.