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