{ "_id": "6a4796932f27a93436c0eca5", "Package": "cgam", "Type": "Package", "Title": "Constrained Generalized Additive Model", "Version": "1.32", "Date": "2026-03-05", "Authors@R": "c(\nperson(\"Mary\", \"Meyer\", email = \"meyer@stat.colostate.edu\", role = \"aut\"),\nperson(\"Xiyue\", \"Liao\", email = \"xliao@sdsu.edu\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-4508-9219\")))", "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. 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