Contains different algorithms and construction methods for efficient Latin hypercube designs (LHDs) with flexible sizes. Our package is comprehensive since it is capable of generating maximin distance LHDs, maximum projection LHDs, and orthogonal and nearly orthogonal LHDs. Detailed comparisons and summary of all the algorithms and construction methods in this package can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <arXiv:2010.09154>. This package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, design of computer experiments.

Contains different algorithms and construction methods for optimal Latin hypercube designs (LHDs) with flexible sizes. Our package is comprehensive since it is capable of generating maximin distance LHDs, maximum projection LHDs, and orthogonal and nearly orthogonal LHDs. Detailed comparisons and summary of all the algorithms and construction methods in this package can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <arXiv:2010.09154>. This package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, design of computer experiments.

The LIC Criterion for Optimal Subset Selection
c(person("Guangbao", "Guo",role = c("aut", "cre"),
email = "ggb11111111@163.com",
comment = c(ORCID = " ")),
person("Yue", "Sun", role="aut"),
person("Guoqi", "Qian", role="aut"),
person("Qian", "Wang", role="aut"))
The LIC criterion is to determine the most informative subsets so that the subset can retain most of the information contained in the complete data. The philosophy of the package is described in Guo G. (2020) <doi: 10.1007/s00180-020-00974-4>.
Guangbao Guo [aut, cre],
Yue Sun [aut],
Guoqi Qian [aut],
Qian Wang [aut]

The LIC criterion is to determine the most informative subsets so that the subset can retain most of the information contained in the complete data. The philosophy of the package is described in Guo G. (2022) <doi:10.1080/02664763.2022.2053949>.