[R-pkgs] pls version 2.0-0

[Bjørn-Helge Mevik and Ron Wehrens]

Version 2.0-0 of the pls package is now available on CRAN.

The pls package implements partial least squares regression (PLSR) and
principal component regression (PCR).  Features of the package include

- Several plsr algorithms: orthogonal scores, kernel pls and simpls
- Flexible cross-validation
- A formula interface, with traditional methods like predict, coef,
  plot and summary
- Functions for extraction of scores and loadings, and calculation of
  (R)MSEP and R2
- Functions for plotting predictions, validation statistics,
  coefficients, scores, loadings, biplots and correlation loadings.

The main changes since 1.2-0 are

- There is now an options mechanism for selecting default fit algorithms.
  See ?pls.options.
- loadingplot() and coefplot() now try to be more intelligent when plotting
  x axis labels.
- The handling of factors in X has been improved, by changing the way the
  intercept is removed from the model matrix.
- All PLSR and PCR algorithms, as well as mvrCv(), have been optimised.
  Depending on the algorithm used, the size of the matrices, and the number
  of components used, one can expect from 5% to 65% reduction in
  computation time.
- Scaling of scores and loadings of kernel PLS and svd PCR algorithm has
  changed.  They are now scaled using the `classic' scaling found in
  oscorespls.
- The arguments `ncomp' now always means "number of components", and `comps'
  always means "component number".  The argument `cumulative' has been
  removed.
- A new data set 'gasoline' has been included.
- The 'NIR' and 'sensory' data sets have been renamed to 'yarn' and 'oliveoil'.


See the file CHANGES in the sources for all changes.

-- 
Bjørn-Helge Mevik