mgcv 1.4 is now on CRAN. It includes new features to allow mgcv::gam to fit almost any (quadratically) penalized GLM, plus some extra smoother classes.
New gam features ------------------------- * Linear functionals of smooths can be included in the gam linear predictor, allowing, e.g., functional generalized linear models/signal regression, smooths of interval data, etc. * The parametric component of a model can be quadratically penalized, giving easy access to gam's fitting and smoothing parameter selection methods, for any model with a penalized glm structure. * Smooths can be linked to have the same estimated smoothing parameter. * `by' variables (used for varying coefficient models) can now be factor variables, to enable easy conditioning of smooths on factors. * The default p-values for smooth terms have been substantially improved. * see ?gam.models and ?summary.gam for further details. New smoothers ---------------------- * Eilers and Marx style P-splines are now built in, along with a cyclic version. See ?p.spline. * An adaptive smoother class has been added. See ?adaptive.smooth. * The interface for adding user defined smooths has been modified and simplified. See ?smooth.construct. A fuller list of changes is at http://cran.r-project.org/web/packages/mgcv/ChangeLog -- > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK > +44 1225 386603 www.maths.bath.ac.uk/~sw283 ------------------------------------------------------- -- > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK > +44 1225 386603 www.maths.bath.ac.uk/~sw283 _______________________________________________ R-packages mailing list [EMAIL PROTECTED] https://stat.ethz.ch/mailman/listinfo/r-packages ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
