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


              CHANGES IN `sda' PACKAGE VERSION 1.3.0

- predict.sda() has been rewritten and is now *much* faster for large 
  numbers of test samples.
- the format of object returned by sda() has changed for more efficient
  prediction.  Note that it is *not* compatible with earlier versions.


              CHANGES IN `sda' PACKAGE VERSION 1.2.4

- License file removed.
- Dependencies updated.
- plot.sda.ranking() is not based on "lattice" graphics any more
  (new code contributed by Sebastian Gibb).
- sda() now allows to specify the shrinkage intensity for the 
  class frequencies.


              CHANGES IN `sda' PACKAGE VERSION 1.2.3

- plot.sda.ranking() now checks for duplicated row names.
- feature.idx argument removed from predict.sda() function. 
- sda.ranking() now allows to specify lambda and lambda.var
  as in the catscore() function.
- sda() also has parameters to set lambda and lambda.var,
  as well as shrink.freqs=TRUE/FALSE.


              CHANGES IN `sda' PACKAGE VERSION 1.2.2

- centroids() function allows to specify the shrinkage intensity 
  for estimating the variances.  Default is now shrinkage rather than
  empirical estimates.
- catscore() also includes options to specifify shrinkage intensities.
  The default is now using shrinkage rather empirical estimates.
- sda.ranking() now uses fdrtool to compute higher criticism scores
- in the output of sda(), the order of entries in the regularization
  vector is now lambda, lambda.var, lambda.freqs.


              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).