Additional arguments provided by `ssr` are `subset`, `scale`,
`limnla` and `control`. `subset` selects a subset of the
data for fitting. `scale`, if T (true), scales all
covariates in the `rk` argument into .
It is recommended that scaling be
done before fitting. `limnla`, a vector of length 1 or 2, sets the
searching limits for on log10 scale when
fitting a univariate smoothing spline model ().
One may fix the smoothing parameter by setting the length of `limnla`
to 1. For example, one may fit a cubic spline with
by

ssr(y~t, rk=cubic(t), limnla=log10(0.01))

The `control` option specifies several control parameters used in
`RKPACK` and `GRKPACK`. See `ssr.control` for details.

Generic utility functions supporting `ssr` include `summary`,
`plot`, `deviance`, `residuals`, and `hat.ssr`,
in addition to the `anova` and `predict` function discussed
in previous sections. The `summary` function provides the basic
description of a `ssr` fit.
`plot` produces diagnostic plots for a `ssr` object.
`hat.ssr` returns the hat matrix of a spline fit.
Note that the full name `hat.ssr` should be used since
the name ``hat'' has been utilized for another purpose.
See the help files in the package for more detailed descriptions.