I have posted a new version of the gam package:  gam_1.09
to CRAN. Thus update improved the step.gam function considerably,
and gives it a parallel option.

I am posting this update announcement along with the original package announcement below,
which may be of interest to those new to the list

Trevor Hastie

Begin forwarded message:

> From: "Trevor Hastie" <hastie@stanford.edu>
> Subject: gam --- a new contributed package
> Date: August 6, 2004 10:35:36 AM PDT
> To: <r-packages@stat.math.ethz.ch>
> 
> I have contributed a "gam" library to CRAN,
> which implements "Generalized Additive Models".
> 
> This implementation follows closely the description in 
> the GAM chapter 7 of the "white" book "Statistical Models in S"
> (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy
> in "Generalized Additive Models" (Hastie & Tibshirani 1990, Chapman and
> Hall). Hence it behaves pretty much like the Splus version of GAM.
> 
> Note: this gam library and functions therein are different from the
> gam function in package mgcv, and both libraries should not be used
> simultaneously.
> 
> The gam library allows both local regression (loess) and smoothing
> spline smoothers, and uses backfitting and local scoring to fit gams.
> It also allows users to supply their own smoothing methods which can
> then be included in gam fits.
> 
> The gam function in mgcv uses only smoothing spline smoothers, with a
> focus on automatic parameter selection via gcv. 
> 
> Some of the features of the gam library:
> 
> * full compatibility with the R functions glm and lm - a fitted gam
>  inherits from class "glm" and "lm"
> 
> * print, summary, anova, predict and plot methods are provided, as
>  well as the usual extractor methods like coefficients, residuals etc
> 
> * the method step.gam provides a flexible and customizable approach to
>  model selection. 
> 
> Some differences with the Splus version of gam:
> 
> * predictions with new data are improved, without need for the
>  "safe.predict.gam" function. This was partly facilitated by
>  the improved prediction strategy used in R for GLMs and LMs
> 
> * Currently the only backfitting algorithm is all.wam. In the earlier
>  versions of gam, dedicated fortran routines fit models that had only
>  smoothing spline terms (s.wam) or all local regression terms
>  (lo.wam), which in fact made calls back to Splus to update the
>  working response and weights. These were designed for efficiency. It
>  seems now with much faster computers this efficiency is no longer
>  needed, and all.wam is modular and "visible"
> 
 
 ----------------------------------------------------------------------------------------
  Trevor Hastie                                   hastie@stanford.edu  
  Professor, Department of Statistics, Stanford University
  Phone: (650) 725-2231                 Fax: (650) 725-8977  
  URL: http://www.stanford.edu/~hastie  
   address: room 104, Department of Statistics, Sequoia Hall
           390 Serra Mall, Stanford University, CA 94305-4065  
 --------------------------------------------------------------------------------------




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