NEWS

---

sda 1.3.9 (2025-04-08)

• updated Authors@R field.
• updated URLs.
• fixed errors flagged by R-devel in documentation.

sda 1.3.8 (2021-11-21)

• update URLs.

sda 1.3.7 (2015-07-08)

• add import statements as required by R-devel.

sda 1.3.6 (2015-03-21)

• fix "S3 generic/method consistency" NOTE raised by R-devel.

sda 1.3.5 (2014-11-18)

• the example R scripts (Khan SRBCT and Singh prostate cancer data) are now provided in R notebook format.
• change of maintainer email address.

sda 1.3.4 (2014-07-28)

• change of maintainer email address.
• corrections to index.html file in inst/doc folder.

sda 1.3.3 (2014-04-18)

• an import() statement has been added to NAMESPACE to address warnings of R 3.1.0.
• added example scripts for Singh et al. (2001) and Khan et al. (2002) gene expression data.
• now suggests "crossval" package for estimating prediction accuracy.

sda 1.3.2 (2014-01-10)

• sda() now also works with a single predictor (previously two predictors were necessary).
• plot.sda.ranking() now has three new options to allow customization (zeroaxis.col, ylab, and main).

sda 1.3.1 (2013-09-01)

• centroids() now also estimates class frequencies (in addition to simply reporting the samples size per class). The frequencies are estimated using a shrinkage approach (set lambda.freqs=0 for empirical estimates). The pooled mean is now computed using the estimated frequencies.
• catscore() now has a lambda.freqs argument and uses shrinkage estimates of class frequencies to compute the scaling factor (to use empirical scaling factors set lambda.freqs=0).
• the estimated frequencies returned by sda() are now contained in the variable "freqs" (which previously was called "prior").
• in sda.ranking() there is now also a lambda.freqs argument
• in addition, sda.ranking() now offers three types of summary statistics for ranking variables in the multi-class case.

sda 1.3.0 (2013-04-28)

• 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.

sda 1.2.4 (2013-04-03)

• 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.

sda 1.2.3 (2012-11-24)

• 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.

sda 1.2.2 (2012-08-20)

• 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.

sda 1.2.1 (2012-01-22)

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

sda 1.2.0 (2011-06-28)

• 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()

sda 1.1.0 (2009-03-12)

• 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

sda 1.0.3 (2008-12-08)

• 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.

sda 1.0.2 (2008-12-03)

• 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.

sda 1.0.1 (2008-11-22)

• 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).

sda 1.0.0 (2008-10-26)

• 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).