cgam (1.15)
Constrained Generalized Additive Model.
http://cran.rproject.org/web/packages/cgam
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 reweighted 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 twodimensional isotonic regression using warpedplane 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) for more details.
Maintainer:
Xiyue Liao
Author(s): Mary C. Meyer and Xiyue Liao
License: GPL (>= 2)
Uses: lme4, Matrix, svDialogs, SemiPar, MASS
Reverse suggests: insight
Released 3 months ago.
13 previous versions
 cgam_1.14. Released 9 months ago.
 cgam_1.13. Released 9 months ago.
 cgam_1.12. Released about 1 year ago.
 cgam_1.11. Released over 1 year ago.
 cgam_1.10. Released almost 2 years ago.
 cgam_1.9. Released almost 2 years ago.
 cgam_1.8. Released over 2 years ago.
 cgam_1.7. Released over 2 years ago.
 cgam_1.6. Released about 3 years ago.
 cgam_1.5. Released over 3 years ago.
 cgam_1.4. Released almost 4 years ago.
 cgam_1.3. Released over 4 years ago.
 cgam_1.2. Released over 5 years ago.
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