cgam: Constrained Generalized Additive Model

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 need 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 wps routine implements a two-dimensional isotonic regression without additivity assumptions.

Version: 1.6
Depends: coneproj (≥ 1.11), svDialogs (≥ 0.9-57), R (≥ 3.0.2)
Suggests: stats, MASS, graphics, grDevices, utils, SemiPar
Published: 2016-11-02
Author: Mary C. Meyer and Xiyue Liao
Maintainer: Xiyue Liao <xiyue at rams.colostate.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: cgam results

Downloads:

Reference manual: cgam.pdf
Package source: cgam_1.6.tar.gz
Windows binaries: r-devel: cgam_1.6.zip, r-release: cgam_1.6.zip, r-oldrel: cgam_1.6.zip
OS X Mavericks binaries: r-release: cgam_1.6.tgz, r-oldrel: cgam_1.6.tgz
Old sources: cgam archive

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