cgam (1.3)

Constrained Generalized Additive Model.

A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors with or without 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 cone information criterion (CIC) may be used to select the best combination of variables and shapes. This package depends on the R package coneproj.

Maintainer: Xiyue Liao
Author(s): Mary C. Meyer and Xiyue Liao

License: GPL (>= 2)

Uses: MASS

Released almost 4 years ago.