RELEASE HISTORY OF THE "sda" PACKAGE ======================================== CHANGES IN `sda' PACKAGE VERSION 1.3.8 - update URLs. CHANGES IN `sda' PACKAGE VERSION 1.3.7 - add import statements as required by R-devel. CHANGES IN `sda' PACKAGE VERSION 1.3.6 - fix "S3 generic/method consistency" NOTE raised by R-devel. CHANGES IN `sda' PACKAGE VERSION 1.3.5 - the example R scripts (Khan SRBCT and Singh prostate cancer data) are now provided in R notebook format. - change of maintainer email address. CHANGES IN `sda' PACKAGE VERSION 1.3.4 - change of maintainer email address. - corrections to index.html file in inst/doc folder. CHANGES IN `sda' PACKAGE VERSION 1.3.3 - 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. CHANGES IN `sda' PACKAGE VERSION 1.3.2 - 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). CHANGES IN `sda' PACKAGE VERSION 1.3.1 - 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. 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).