Imports: stats, methods, Matrix (>= 1.1-5)

Title: Modelling with Sparse and Dense Matrices

Imports: stats, methods, Matrix (>= 1.4-2)

Authors@R: c(person("Julien", "Neves", email = "jmn252@cornell.edu", role = c("aut", "cre")), person("Jean-Marie", "Dufour", email = "jean-marie.dufour@mcgill.ca", role = c("aut")))
Description: An implementation of the Monte Carlo techniques described in details by Dufour (2006) <doi:10.1016/j.jeconom.2005.06.007> and Dufour and Khalaf (2007) <doi:10.1002/9780470996249.ch24>. The two main features available are the Monte Carlo method with tie-breaker, mc(), for discrete statistics, and the Maximized Monte Carlo, mmc(), for statistics with nuisance parameters.
Suggests: fUnitRoots, microbenchmark, boot, MASS, knitr, rmarkdown
Imports: GenSA, pso, GA, NMOF, scales, stats, graphics, utils

Description: Generate a maximum projection (MaxPro) design, a MaxPro Latin hypercube design or improve an initial design based on the MaxPro criterion. Details of the MaxPro criterion can be found in: Joseph, V. R., Gul, E., and Ba, S. (2015) "Maximum Projection Designs for Computer Experiments", Biometrika.

Description: Generate maximum projection (MaxPro) designs for quantitative and/or qualitative factors. Details of the MaxPro criterion can be found in: (1) Joseph, Gul, and Ba. (2015) "Maximum Projection Designs for Computer Experiments", Biometrika, 102, 371-380, and (2) Joseph, Gul, and Ba. (2018) "Designing Computer Experiments with Multiple Types of Factors: The MaxPro Approach", Journal of Quality Technology, to appear.

Title: Orthogonal Data Projections with Maximal Skewness
Author: Cinzia Franceschini, Nicola Loperfido
Description: It finds Orthogonal Data Projections with Maximal Skewness. The first data projection in the output is the most skewed among all linear data projections. The second data projection in the output is the most skewed among all data projections orthogonal to the first one, and so on.

Author: Cinzia Franceschini and Nicola Loperfido