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.

**rpart**
(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,
Rwave, waveslim,
wavethresh).

Last Modified: 20 April 2005 by J. Fox <jfox AT mcmaster.ca>