1.1-7
* bug fix in scam which could fail when the smoothing parameter values are supplied in 'sp'
argument to 'scam'. ('sp' values supplied to the smooth term 's' are ignored by 'scam')
Thanks to Noel G. Cadigan for reporting the bug.
* References fixes in .Rd files
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