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
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