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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.



##pendings
+ multivariate version
+ residual structures
