           

           REVISION HISTORY OF "corpcor" PACKAGE


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()
- Toutines have been modified so that data sets with zero-variance
  variables may also be analysed (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 a "weights" argument.
- 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.


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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 
