Generalized Additive Models with Flexible Response Functions
Description
Standard generalized additive models assume a response function,
which induces an assumption on the shape of the distribution of the
response. However, miss-specifying the response function results in biased
estimates. Therefore in Spiegel et al. (2017)
we propose to estimate the response function
jointly with the covariate effects. This package provides the underlying
functions to estimate these generalized additive models with flexible
response functions. The estimation is based on an iterative algorithm. In
the outer loop the response function is estimated, while in the inner loop
the covariate effects are determined. For the response function a strictly
monotone P-spline is used while the covariate effects are estimated based on
a modified Fisher-Scoring algorithm. Overall the estimation relies on the
'mgcv'-package.