1.1-6

* Namespace, Description fixed

* vis.scam: added, copied from vis.gam(mgcv) 

* scam: added additional returned elements of the fitted scam object: `aic', `df.residual', `df.null', `min.edf', object$call (needed for update to work), 'var.summary' (needed for vis.scam), `R', `edf1'.
   added keepData argument as input, if FALSE (default) the original data removed to save space. 
   removed object$X (model matrix) from return/output list to save space. model.matrix() added as in gam(), correspondingly summary.scam() and predict.scam() were corrected to replace object$X by model.matrix(object). 

* scam.fit: `Rrank' is now an exported object of mgcv 

* scam.check: added extra graphics parameters (...) to pass to plotting functions (like pch,cex);  added qqline() with colours in qqnorm() plot

* plot.scam: corrected number of plots per page and plotting any parametric terms when "all.terms=TRUE" as in plot.gam() (although plot.scam is based on the old version of plot.gam()).

* derivative.scam: this function replaced derivative.smooth(). It
works only for univariate smooths at the moment, with finite differencing approximations for unconstrained smooths.

* summary.scam: corrected the value of the adjusted R-sq as in gam(mgcv), fixed Ref.df to be the same as in summary.gam(),
  the code was corrected to be in line with summary.gam()

* scam.fit.post: corrected the value of the null deviance, added the null degrees of freedom, removed the value `TRUE' for intercept to correct the values of the deviance explaned and null.df for models without intercept.

* extrapolate.uni.scam: this function has been removed from the package since predict.scam does linear extrapolation



1.1-5

* Predict.matrix.**.smooth 

For all univariate and bivariate SCOP-splines Prediction method now allows prediction outside range of knots, and use linear extrapolation in this case



1.1-4

* scam.fit: svd replaced with QR + svd if needed approach, 
  large number of O(nq^2) products that were not needed removed, removal of multiple un-necessary loops.

* gcv.ubre_grad tidied, terms collected in derivative of trace (8 goes to 6), derivative of trace computations restuctured to reduce O(nq^2) operations from 6 per derivative to 4 up front.

* predict.scam: line for reference to extrapolate.uni.scam corrected with "if.. " for uni case only

