  We present a method of variable selection for the sparse generalized additive
model. The method doesn't assume any specific functional form, and can select
from a large number of candidates. It takes the form of incremental forward
stagewise regression. Given no functional form is assumed, we devised an
approach termed roughening to adjust the residuals in the iterations. In
simulations, we show the new method is competitive against popular machine
learning approaches. We also demonstrate its performance using some real
datasets. The method is available as a part of the nlnet package on CRAN
https://cran.r-project.org/package=nlnet.
