Dear R community,

The hier.part package applies hierarchical partitioning based upon a GLM
(generalized linear model) framework to assess the independent and joint
effect from a set of predictors onto a single quantitative response
variable. Conceptually and from a statistical point of view, are there any
problems to adapt the philosophy of hierarchical partitioning under a GAM
(generalized additive model) framework, or more generally to any other
non-linear modelling framework such as neural networks or regression trees?

Best regards

Clement

	[[alternative HTML version deleted]]


