We present a suite of user friendly S functions for fitting
various smoothing spline models including (a) non-parametric regression
models for independent and correlated Gaussian data, and for
independent binomial, Poisson and Gamma data;
(b) semi-parametric linear mixed-effects models;
(c) non-parametric nonlinear regression models;
(d) semi-parametric nonlinear regression models; and
(e) semi-parametric nonlinear mixed-effects models. The general form of
smoothing splines based on reproducing kernel Hilbert spaces is used to
model non-parametric functions. Thus these S functions deal with
many different situations in a unified fashion. Some well known
special cases are polynomial splines, periodic splines, spherical
splines, thin-plate splines, l-splines, generalized additive models,
smoothing spline ANOVA models, projection pursuit models, multiple
index models, varying coefficient models, functional linear models,
and self-modeling nonlinear regression models. These
non-parametric/semi-parametric linear/nonlinear fixed/mixed models
are widely used in practice to analyze data arising in many areas of
investigation such as medicine, epidemiology, pharmacokinetics, econometrics
and social science. This manual describes technical details behind
these S functions and illustrate their applications using several
examples.
Supported by NIH Grant R01 GM58533.
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