[R-pkgs] pls version 2.1-0

[Bjørn-Helge Mevik and Ron Wehrens]

Version 2.1-0 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, wide 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, and correlation loadings.

The main changes since 2.0-0 are

- Jackknife variance estimation of regression coefficients has been added.

- The `wide kernel' PLS algorithm has been implemented.  It is faster than the
  other algorithms for very wide data.

- The definition of R^2 has been changed to  1 - SSE/SST for all estimators,
  so R2() will give different results for test sets and
  cross-validation compared to pls < 2.1-0.  Also, the internal
  calculations have been reorganised.

- The plot functions for coefficients, predictions and validation results
  (R2, (R)MSEP) have gained an argument `main' to set the main title of the
  plot.

- plots that go over several pages now only set `par(ask = TRUE)' if the plot
  device is interactive (suggested by Kevin Wright).

- mvr() and mvrCv() now check for near zero standard deviation when autoscaling
  (`scale = TRUE')


See the file CHANGES in the sources for all changes.

-- 
Bjørn-Helge Mevik and Ron Wehrens