
           

           REVISION HISTORY OF THE "corpcor" PACKAGE


Version 1.4.8

- new "collapse" option in cor.shrink, cov.shrink, invcor.shrink, invov.shrink
  to allow memory savings when lambda equals 1.
- to simplify code base two rarely used options were removed:
  "protect" and  "scale.by" (wt.scale is now always done using "sd").
- package depends now on R 2.7.0.
- documentation was polished.


Version 1.4.7

- change of license from "GPL 2 or later", to "GPL 3 or later".


Version 1.4.6

- following a suggestion (and a patch) by Nicola Soranzo internal big 
  objects are now explicitely removed when they are not needed anymore.  
  As a result, the package now needs less computer memory, and
  larger (partial) correlation matrices can be computed.


Version 1.4.5

- when partial correlations are computed using pcor.shrink() the
  returned matrix now has the standardized partial variances (i.e. 
  PVAR/VAR) attached under the attribute "spv".
- updated references in the help pages


Version 1.4.4

- the function wt.scale() is now *much* faster, especially for large p,
  due to using colSums() rather than apply() .. note that this indirectly 
  speeds up most other functions in the corpcor package!
- typos in the documtation were corrected and references updated


Version 1.4.3

- the shrinkage target for the variance is now the median (previously,
  variances were shrunken towards the mean).
- var.shrink now also has a"protect" argument. 
- limited translation shrinkage is now turned off by default 
   (i.e. protect has value zero). 
  

Version 1.4.2

- limited translation estimator implemented for the shrinkage estimate
  of the correlation matrix. This prevents excessive component risk.
- new functions for decomposing the covariance matrix and
  its inverse: decompose.cov(), decompose.invcov(), rebuild.invcov()
- new function pvar.shrink() to estimate partial variance.
- in the documention the definition of partial variance and 
  partial covariance are corrected (following Whittaker 1990)  
- the functions cov2pcov(), pcov2cov(), pcov.shrink() have been removed.
- functions sm2vec(), vec2sm(), sm.index() back (from GeneTS)
- is.positive.definite() checks for complex eigenvalues.


Version 1.4.1:

- fast.svd() now doesn't use any more the LAPACK routines DGESVD
  to compute the singular value decomposition (this routine is depricated
  from R version 2.3.0)
  
- weighted.var(), weighted.moments(), weighted.scale() are
  now called wt.var(), wt.moments(), wt.scale() 


Version 1.4.0:

- New functions mvr.shrink() and mvr.predict() for multivariate
  shrinkage regression.
- All shrinkage estimate now carry the class attribute "shrinkage.
  This allows for a more informative output via print.shrinkage()
- Removed functions:  sm2vec(), vec2sm(), sm.indexes()


Version 1.3.1:

- This versions fixes a bug present in "corpcor" version 1.3.0 
  and 1.2.0 but not in earlier versions.  This bug leads to a 
  (probably negligible) small bias in the computation of the 
  optimal shrinkage intensity. 
- The functions cov.bagged(), cor.bagged(), and pcor.bagged()
  have been removed.
- Typographical errors in the documentation were corrected.


Version 1.3.0:

- New function "var.shrink" to compute shrinkage estimates of 
  variances (target: average empirical variances.
- cov.shrink() and pcov.shrink() are now also based on shrunken 
  variances.
- Estimation of shrinkage intensities are now done in C.
  This greatly decreases the computational costs.
- Options "check.eigenvalues" and "exact.inversion" have been 
  removed in cor2pcor() and pcor2cor()
- The functions have been modified so that data sets with 
  zero-variance variables may also be analyzed (these will be in 
  effect ignored in estimating correlation but taken into account
  when estimating variances).


Version 1.2.0:

- Greatly reduced memory and faster computations.
- New code on fast inversion using Woodbury identity.
- Consequently, pcor.shrink() is now much faster .
- New functions for computation of weighted variances,
  weighted moments, and weighted rescaling.
- All covariance etc. estimators now also have the option to
  supply data weights".
- varcov() function removed (not necessary any more).
- Several parts of documentation updated.
- Juliane's Web address updated.


Version 1.1.2:

- Minor typos in documentation corrected.
- From this version is.positive.definite() works with
  arbitrary matrix (previously it required symmetric matrix).

  
Version 1.1.1:

- Reference to shrinkage covariance paper is updated.


Version 1.1:

- cor.shrink() is now the central estimator, and cov.shrink
  is derived.


Version 1.0:

- First stand-alone release of the functions for 
  computing (partial) correlation and covariance.
  Prior to this versions these functions were part
  of the GeneTS package.


#################################################################

Prior to 1.0.0:

- In cor2pcor() and pcor2cor() input standardization removed.
- LAPACK.svd function (with automatic choice of svd algorithm)
- Now fast.svd, ggm.simulate.data, rank.condition uses LAPACK.svd 
