RELEASE HISTORY OF THE "sda" PACKAGE
             ========================================


              CHANGES IN `sda' PACKAGE VERSION 1.2.1

- NAMESPACE file added
- updated requirements for "corpcor" and "entropy"


              CHANGES IN `sda' PACKAGE VERSION 1.2.0

- requires now corpcor 1.6.0 and R version 2.10.0 
- new function catscore()
- centroids() function has been streamlined and simplified
- updated documentation
- employs function crossprod.powcor.shrink() of corpcor
  which leads to reduced memory imprint and increased speed
  in functions catscore(), sda.ranking() and sda()


              CHANGES IN `sda' PACKAGE VERSION 1.1.0 

- new sda.ranking() function
- plot function for "sda.ranking" objects 
- additional to FDR values computation of higher-criticism scores
- reference to Ahdesm\"aki and Strimmer (2009) paper added
- Singh et al. (2002) example data added
- improved help pages and examples
- the data khan.x is now on log-scale


              CHANGES IN `sda' PACKAGE VERSION 1.0.3 

- sda() now provides ranking of features.
- fdr values can optionally be computed for each feature.
- centroids() now reports number of samples and features.
- sda() function has been rewritten, and a bug introduced in 
  version 1.0.2 has been corrected.


              CHANGES IN `sda' PACKAGE VERSION 1.0.2 

- predict.sda() is now very much faster, and the object returned
  by sda() needs much less memory.  
- the centroids() function now additionally computes the pooled mean and 
  arbitrary powers of the correlation matrix (not just alpha=-1).
- the microarray data from Khan et al. 2001 are now used as example.
- bug fix: for shrinkage DDA the inverse correlation matrix is not
  computed unnecessarily any more.


              CHANGES IN `sda' PACKAGE VERSION 1.0.1 

- new centroids() function to compute group-wise centroids,
  (pooled variances), and inverse pooled correlations. 
- uses the "collapse" option in corpcor >= 1.4.8 to save
  memory when estimated correlation is diagonal (effectively
  turning LDA into DDA if the estimated shrinkage intensity lambda=1).


              CHANGES IN `sda' PACKAGE VERSION 1.0.0


This package implements LDA and DDA classification,
where the training of the classifier is done via Stein-type
shrinkage of frequencies, variances, and correlation.

This approach is particularly suitable for high-dimensional 
classification.


This is the first public release (27 October 2008).