'CGAL' is a C++ library that aims to provide easy access to efficient and
  reliable algorithms in computational geometry. Since its version 4, 'CGAL' can be used
  as standalone header-only library and is available under a double GPL-3|LGPL license.
  <https://www.cgal.org/>.

A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions.  It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Liao X, Meyer MC (2019)<doi:10.18637/jss.v089.i05> for more details.

The cgAUC can calculate the AUC-type measure of Obuchowski(2006) when gold standard is continuous, and find the optimal linear combination of variables with respect to this measure.

Developing general equilibrium models, computing general equilibrium and simulating economic dynamics with structural dynamic models in LI (2019, ISBN: 9787521804225) "General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press". When developing complex general equilibrium models, GE package should be used in addition to this package.

Run computer experiments using the adaptive composite grid
    algorithm with a Gaussian process model.
    The algorithm works best when running an experiment that can evaluate thousands
    of points from a deterministic computer simulation.
    This package is an implementation of a forthcoming paper by Plumlee,
    Erickson, Ankenman, et al. For a preprint of the paper,
    contact the maintainer of this package.