           

           REVISION HISTORY OF THE "corpcor" PACKAGE


Version 1.4.0:

- New dunctions 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.


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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 
