Shape constrained additive models
Description
Routines for generalized additive modelling under shape
constraints on the component functions of the linear predictor.
Models can contain multiple shape constrained (univariate
and/or bivariate) and unconstrained terms. The routines of
mgcv(gam) package are used for setting up the model matrix,
printing and plotting the results. Penalized likelihood
maximization based on Newton-Raphson method is used to fit a
model with multiple smoothing parameter selection by GCV or
UBRE/AIC.