• Now derive a better estimate of the total variance, to get better estimates of the variance explained by each PC, especially when using clumping.
  • read.pcadapt() generates bed files instead of pcadapt files.

  • Computation of PCA is now based on R package RSpectra.

  • Missing values are handled by specifying matrix-vector operations in RSpectra that accounts for missing values.

  • Includes LD thinning to compute PCs.

  • No more dependency to R package RcppArmadillo.

  • For Pooled-seq data, use Mahalanobis distances based on PCA loadings, no more simulations of individual genotypes.

  • Switch from C/Lapack to Rcpp/RcppArmadillo.

  • pcadapt() can take genotype matrices as input.

  • Modified code for binomial sampling.

  • pcadapt() argument clean.files is now deprecated.

  • pcadapt() argument output.filename is now deprecated.

  • read.pcadapt() argument local.env is now deprecated.

  • Latest update of R package vcfR taken into account.

  • Method based on sampling genotypes added to handle pooled-sequencing.
  • Option type = "vcfR" has been added to read.pcadapt() to overcome some conversion issues occurring with VCF files.

  • Argument transpose is now deprecated. Read section A for more details.

  • The function get.pc() has been added. For each SNP, it returns the most correlated principal component.
  • Function read4pcadapt() is now deprecated, it is now called read.pcadapt().

  • Using the pop option when plotting scores now provides the color legend.

  • All analyses are now included in the R package. Users should not use the C software PCAdapt fast anymore.

  • Big datasets can be handled directly within the R session.

  • read4pcadapt() now converts files to the pcadapt format.

  • The first argument of pcadapt() can be either a small genotype matrix or the output of read4pcadapt().

  • The Mahalanobis distance is now estimated from the z-scores rather than the loadings.

  • Make sure you have downloaded the latest version of the C software PCAdapt (last updated on February 11, 2016).

  • The scaling of the SNP before computing PCA has been changed. Instead of using standard deviation, we now use the square root of p(1−p) (haploid data) or of 2p(1−p) (diploid data) where p is the minimum allele frequency.

  • Bug fix: the genomic inflation factor has been corrected when K=1.

  • Bug fix: a problem due to high proportion of missing data slowing the program has been fixed.

  • Argument "minmaf" has been replaced with "min.maf".

  • The default test statistic is not the communality statistic anymore but the Mahalanobis distance.

  • Test statistic for Pool-seq data.