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


              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).

