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version 1.1 (2016-01-24)
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  Initial release
  
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version 1.2 (2016-02-07)
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  + 'fdr' function bug was fixed
  + addition of the 'randef' function
  + addition of the converter 'atcg1234' function 
  + names in the blup's or random effects added
  + zero-boundary constraint added to Average Information algorithm
     - it finds which var.comps are pushed to zero constantly
     - recalculates variance components removing such components 
     - fix those values and calculates the most likely value for 
       the problematic var.comp  
  + now 'mmer2' can handle missing data in explanatory variables as lmer
  + now summary of 'mmer2' has names in the variance components
  + A.mat, D.mat and E.mat supported for polyploids
  + mmer can run GWAS for polyploid organisms
     - the models implemented are the same than Rosyara (2016):
     - "additive","1-dom-alt","1-dom-ref","2-dom-alt","2-dom-ref"
  + eigen decomposition to accelarate genomic prediction based on Lee (2015)
    has been added in the argument 'MTG2' of the AI, mmer and mmer2 algorithm
  
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version 1.3 (2016-03-01)
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  + The 'bag' function for bagging-GBLUP from Abdollahi-Arpanahi et al. (2015) 
    has been added:
        - The function takes a model fitted and creates a bag matrix with 
          the top markers (most significant) and creates a design matrix 
          to be used as fixed effects in the GBLUP model to increase 
          prediction accuracy.
  + 'bag' function has been equiped with stepwise selection to make sure that markers
    selected by "clustering" or "maximum" p.values methods provide at least a minimum
    increase in the prediction accuracy.
  + The Fisher Information matrix can be returned from the mmer function when 
    the AI is used (default) but the argument 'Fishers' needs to be set to TRUE.
  + The bug for the AI algorithm when one var.comp and K and Z are diagonal has been 
    fixed by changing to EMMA in this naive situation.
  + AI algorithm has been debuged to return the most likely variance components when the 
    likelihood takes values around the maximum in a zig zag pattern. Just takes the value 
    where the ML was found. When the likelihod follows a scale and dropping pattern
    the program will do the same. A warning message is emmitted.
  + GWAS modality of 'mmer' now adds the names of the markers of each score to keep track
    the value for each marker.

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version 1.4 (2016-04-15)
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  + The AI algorithm will take 5 EM steps if after 10 iterations (AI) the likelihood drops 
    suddenly, indicating that initial values were too far from real values causing a bad 
    behavior of the likelihood. The EM steps aim to provide initials values for those 
    problematic variance components. ONLY mmer2!!!!!!
  + The AI likelihood behaving in a zig zag pattern is detected only after 10 iterations 
    and we opted for returning the ML estimators.
  + Minor bugs have been fixed (names in random terms). In addition, ordering random effects
    based on their degrees of freedom has been implemented to provide more stability to the 
    AI algorithm.

##pendings
+ multivariate version
+ residual structures
