This is not meant to be an exhaustive list. Boldfaced functions and packages are of special interest (in my opinion). See the web appendix on Nonparametric Regression from my R and S-PLUS Companion to Applied Regression (Sage, 2002) for a brief introduction to nonparametric regression in R.
ksmooth: Coordinates for kernel-regression scatterplot smoother.
loess: Nearest-neighbour local-polynomial regression.
loess.smooth: Coordinates for nearest-neighbour local-polynomial scatterplot smoothing.
lowess: Coordinates for nearest-neighbour local-polynomial scatterplot smoothing.
ppr: Projection-pursuit regression.
scatter.smooth: Scatterplot with loess smooth.
smooth.spline: Coordinates for smoothing-spline scatterplot smoother.
supsmu: Coordinates for the "super-smoother" scatterplot smoother.
locpoly (in package KernSmooth): Local polynomial scatterplot smoother.
gam (in mgcv): Generalized additive models with degrees of freedom of terms selected by generalized cross-validation; uses smoothing splines.
nnet (in nnet): Neural networks; associated with Venables and Ripley (2002).
bs and ns (in splines): Generate B-spline and natural-spline regression-spline bases for use in linear and generalized-linear (etc.) models.
pspline (in survival): Smoothing splines for Cox survival-regression models.
(in rpart): Classification
and regression trees.
ace and avas (in acepack): Finds linearizing nonparametric transformations of the response and predictors in a regression, using the ACE (alternating conditional expectations) or AVAS (additivity and variance stabilization) algorithms.
nnr, slm, snm, snr, ssr (in assist): Functions for fitting a variety of spline-based nonparametric and semi-parametric regression models.
aws (in aws): Local-polynomial regression with up to three predictors.
cobs (in cobs): Constrained b-spline quantile scatterplot smoother.
gam (in gam): Generalized additive models using smoothing splines or local polynomial regression; associated with Hastie and Tibshirani (1990).
ssanova and gssanova (in gss): additive and generalized-additive regression models using splines.
ipredbagg in (ipred): Improved predictions by "bagging" for classification and regression trees.
lpridge and lpepa (in lpridge): Local polynomial scatterplot smoother.
locfit (in locfit): Local polynomial regression; associated with Loader (1999).
glkerns and lokerns (in lokern): Kernel-regression scatterplot smoothers.
bruto and mars (in mda): Additive regression models.
polymars (in polspline): Multivariate additive regression models.
smooth.Pspline and sm.spline (in pspline): Smoothing-spline scatterplot smoother.
randomForest (in randomForest): Classification and regression trees.
rqss (in quantreg): Additive quantile-regression models (e.g., for median, quartiles, etc.).
sm.regression and others (in sm): Local linear regression with one or two predictors; associated with Bowman and Azzalini (1997).
tree in (tree): classification and regression trees (similar to rpart); associated with Venables and Ripley (2002)
As well, the fields
and spatial packages for spatial
data analysis (the latter included in the standard R distribution, and associated
with Venables and Ripley, 2002) provide for smoothing. There are also several
packages for wavelet smoothing (ebayesthresh,
Last Modified: 20 April 2005 by J. Fox <jfox AT mcmaster.ca>