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.

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.

Orthogonal Data Projections with Maximal Skewness
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.

Cinzia Franceschini and Nicola Loperfido

Selecting the Best Set of Relevant Environmental Variables along
with the Optimal Regularization Multiplier for Maxent Niche
Modeling
Complex niche models show low performance in identifying
the most important range-limiting environmental variables and in
transferring habitat suitability to novel environmental conditions
(Warren and Seifert, 2011; Warren et al., 2014). This package helps to
identify the most important set of uncorrelated variables and to
fine-tune Maxent's regularization multiplier. In combination, this
allows to constrain complexity and increase performance of Maxent niche
models (assessed by information criteria, such as AICc (Akaike, 1974) ,
and by the area under the receiver operating characteristic (AUC)
(Fielding and Bell, 1997). Users of this package should be familiar with
Maxent niche modelling.

Complex niche models show low performance in identifying
the most important range-limiting environmental variables and in
transferring habitat suitability to novel environmental conditions
(Warren and Seifert, 2011 <DOI:10.1890/10-1171.1>; Warren et al.,
2014 <DOI:10.1111/ddi.12160>). This package helps to identify the
most important set of uncorrelated variables and to fine-tune Maxent's
regularization multiplier. In combination, this allows to constrain
complexity and increase performance of Maxent niche models (assessed
by information criteria, such as AICc (Akaike, 1974
<DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver
operating characteristic (AUC) (Fielding and Bell, 1997
<DOI:10.1017/S0376892997000088>). Users of this package should be
familiar with Maxent niche modelling.