The 'minimaxdesign' package provides two main functions: mMcPSO() and
miniMaxPro(), which generates minimax designs and minimax projection designs using
clustering and particle swarm optimization (PSO) techniques. These designs can be used
in a variety of settings, e.g., as space-filling designs for computer experiments or
sensor allocation designs. A detailed description of the two designs and the employed
algorithms can be found in Mak and Joseph (2016).
Rcpp (>= 0.12.4), randtoolbox, DiceDesign, MaxPro, doParallel,
gtools, nloptr, foreach

Provides two main functions: mMcPSO() and
miniMaxPro(), which generates minimax designs and minimax projection designs using
a hybrid clustering - particle swarm optimization (PSO) algorithm. These designs can be used
in a variety of settings, e.g., as space-filling designs for computer experiments or
sensor allocation designs. A detailed description of the two designs and the employed
algorithms can be found in Mak and Joseph (2016) <https://arxiv.org/abs/1602.03938>.
Rcpp (>= 0.12.4), randtoolbox, DiceDesign, MaxPro, doParallel,
doSNOW, gtools, nloptr, foreach

Provides two main functions: mMcPSO() and
miniMaxPro(), which generates minimax designs and minimax projection designs using
a hybrid clustering - particle swarm optimization (PSO) algorithm. These designs can be used
in a variety of settings, e.g., as space-filling designs for computer experiments or
sensor allocation designs. A detailed description of the two designs and the employed
algorithms can be found in Mak and Joseph (2017) <DOI:10.1080/10618600.2017.1302881>.

Rcpp (>= 0.12.4), randtoolbox, DiceDesign, MaxPro, doParallel,
doSNOW, gtools, nloptr, foreach, jpeg

Provides two main functions, minimax() and miniMaxPro(), for computing minimax
and minimax projection designs using the minimax clustering algorithm in Mak and
Joseph (2018) <DOI:10.1080/10618600.2017.1302881>. Current design region options
include the unit hypercube ("hypercube"), the unit simplex ("simplex"), the unit ball
("ball"), as well as user-defined constraints on the unit hypercube ("custom"). Minimax
designs can also be computed on user-provided images using the function minimax.map().
Design quality can be assessed using the function mMdist(), which computes the minimax
(fill) distance of a design.
Rcpp (>= 0.12.4), randtoolbox, DiceDesign, MaxPro, doParallel,
doSNOW, gtools, nloptr, foreach, jpeg, gmp, conf.design, pdist,
DoE.base, FrF2