
		CHANGES IN GeneTS VERSION 2.1.2

CHANGES

    o edge colors associated with positive and negative correlation
      can now be specified in ggm.plot.graph().
      
    o plotting of edge labels can now be turned off on request.

    o internal detection of the version of the installed graph and Rgraphviz
      libraries is improved.



		CHANGES IN GeneTS VERSION 2.1.1

CHANGES

    o in ggm.plot.graph() a small change is introduced to make it work
      with Rgraphviz version 1.3.25 (layoutType is explicitly specfied
      when getDefaultAttrs() is called).


		CHANGES IN GeneTS VERSION 2.1
		
		
This release contains changes to make GeneTS work with the 1.4 release of
Bioconductor (in particular with the updated Rgraphviz package in BioC 1.4).
Note that GeneTS 2.1 should work both with BioC 1.3 and BioC 1.4. 
This release also corrects several small optimization problems present
in GeneTS 2.0.1.  

CHANGES
    
    o dcor0() now works correctly also for large values of kappa.
      In previous versions dcor0() returned NaN for kappa > 170.
      Similarly, pcor0() can now also be used for large values of kappa.
      In addition, dcor0() and pcor0() now always return the correct values
      (instead of NaNs) at the boundaries r=-1 and 1, and for r=0.
	 
    o in cor.fit.mixture() the upper bound MAXKAPPA for the estimated
      kappa can now be specified as optional argument. In addition,
      a warning message is issued if the estimated kappa equals MAXKAPPA.
      The default for MAXKAPPA is 5000. The minimum value of kappa is now 1
      (used to be 2). Furthermore,  the estimated degree of freedom kappa is
      not rounded to an integer value any more.
      
    o cor0.estimate.kappa() now also has a MAXKAPPA option and also doesn't
      round estimates of kappa any more.
       
    o ggm.test.edges() now also has a MAXKAPPA option.  The eta0 estimated
      in this function is now explicitely used in determining the q-values
      (previously, the conservative choice eta0=1 was assumed). 

    o the "ts" and "modreg" packages needed by GeneTS are now loaded only
      when running R versions <= 1.8.1.  From R version 1.9 these packages
      are merged into the default "stats" package.
    
    o  ggm.plot.graph(), ggm.make.graph() and show.edge.weights() now check
       whether the "graph" and the "Rgraphviz" packages are available, and
       if not return an error message.
      
    o  if BioC 1.4 is used ggm.plot.graph() plots edges with negative correlation
       in grey color (postive partial correlation is plotted in black color).  


NEW FUNCTIONS

    o pcor() is renamed to partial.cor() to avoid name collision with a
      function of the same name in the package "ggm".
  
    o show.edge.weights() summarizes a graph objects by printing the vector
      of edge weights 
      
    

		CHANGES IN GeneTS VERSION 2.0.1
		
		
This is a bug-fix release that corrects several small problems present in
GeneTS 2.0.  It also contains changes to make GeneTS more compatible with
the upcoming version 1.9 release of R.

CHANGES

    o help pages for ggm.estimate.pcor, ggm.simulate.pcor, ggm.simulate
      data were changed (5% replaced with 5 percent), the unused argument
      "p" from documenentation in dcor0 was removed, and a reference in
      fdr.control was corrected
    
    o the example session for inferring GGMs was slightly changed (to fix a
      bug and to make it easier to run a similar analysis for own data)

    o in bag.fun results containing NAs are now flagged as erroneous so that
      the corresponding bootstrap draws are repeated in robust.boot

    o a confusing warning message in robust.boot was removed
    
    o the robust bootstrap is now halted when there are more than R errors
    
    o the functions fisher.g.test, is.constant, dominant.freqs,
      periodogram.spec, and periodogram.freq now also take data frames
      (and not just matrices and vectors)
   
    
		CHANGES IN GeneTS VERSION 2.0


NEW FEATURES

    With version 2.0 GeneTS provides functions to infer large sparse graphical
    Gaussian models, following an approach outlined in:

    Schaefer, J., and K. Strimmer. 2003. A practical approach to inferring large
    graphical models from sparse microarray data. Submitted to Bioinformatics.
    [Preprint available from http://www.stat.uni-muenchen.de/~strimmer/]

NEW FUNCTIONS
 
    o ggm.simulate.pcor, ggm.estimate.pcor, ggm.simulate.data, ggm.test.edges
    
    o ggm.make.graph, ggm.plot.graph

    o cor2pcor, pcor2cor, pcor
 
    o bagged.cov, bagged.cor, bagged.pcor  
   
    o cor0.test, cor0.estimate.kappa, cor.fit.mixture, cor.prob.nonzero
    
    o kappa2N, N2kappa
   
    o dcor0, pcor0, rcor0, ibeta functions
 
    o z.transform, hotelling.transform
    
    o robust.boot

    o fdr.estimate.eta0

    o pseudoinverse, rank.condition

    o standardize.cov, rebuild.cov
    
    o sm2vec, sm.indices, vec2sm 
 
    o is.positive.definite, is.symmetric, is.square
   
 
   
    
CHANGES

    o bacteria renamed to caulobacter to avoid collision with MASS package.
    
    o fdr.control renamed from fdr.test,  significant hypothesis are 
      now those with p <= cutoff rather than with p < cutoff,
      q-values are computed, and pi0 < 1 is now allowed.
 
   


		CHANGES IN GeneTS VERSION 1.1


NEW FEATURES

    This version of GeneTS is a maintainance release only.

CHANGES


    o The functions periodogram.spec() and periodogram.freq() are now
      combined in the single function periodogram(). 

    o Rather than using angular frequencies between 0 and pi, GeneTS
      by default now uses frequencies between 0 and the Nyquist critical
      frequency fc = frequency()/2 (i.e. typically between 0 and 0.5). 

    o Given a constant time series fisher.g.test() now returns a p-value
      of 1 instead of issuing an error. The man page for fisher.g.test()
      is changed and slightly extended.

    o Previously, all methods in GeneTS assumed that the *rows* in a matrix
      contain the time series data.  This convention is now changed so that
      each *column* represents one time series (this follows the convention
      used by the ts package).  All functions and data sets in GeneTS are
      also changed accordingly.
    



		CHANGES IN GeneTS VERSION 1.0


NEW FEATURES

    First public release of GeneTS.  This version of GeneTS implements
    all methods for cell cycle analysis described in:

    Wichert, S., K. Fokianos, and K. Strimmer. 2004.  Identifying periodically
    expressed transcripts in microarray time series data. Bioinformatics 20:5-20.

NEW FUNCTIONS
      
      o the average periodogram for multiple time series (avgp)
      
      o Fisher's exact g test as gene selection method (fisher.g.test)
      
      o multiple testing using false discovery rate (fdr.test)
      
      o simplified interface to periodogram (periodogram)
      
      o some other utility function (is.constant, dominant.freqs)
      
      o parts of the bacterial data set by Laub et al. (2000) (caulobacter)
 
