Fast implementations to compute the genetic covariance matrix, the Jaccard similarity matrix, the smatrix (the weighted Jaccard similarity matrix), and the (classic or robust) genomic relationship matrix of a (dense or sparse) input matrix (see Hahn, Lutz, Hecker, Prokopenko, Cho, Silverman, Weiss, and Lange (2020) <doi:10.1002/gepi.22356>). Full support for sparse matrices from the Rpackage 'Matrix'. Additionally, an implementation of the power method (von Mises iteration) to compute the largest eigenvector of a matrix is included, a function to perform an automated full run of global and local correlations in population stratification data, a function to compute sliding windows, and a function to invert minor alleles and to select those variants/loci exceeding a minimal cutoff value. New functionality in locStra allows one to extract the k leading eigenvectors of the genetic covariance matrix, Jaccard similarity matrix, smatrix, and genomic relationship matrix via fast PCA without actually computing the similarity matrices. The fast PCA to compute the k leading eigenvectors can now also be run directly from 'bed'+'bim'+'fam' files.
Package details 


Author  Georg Hahn [aut,cre], Sharon M. Lutz [ctb], Christoph Lange [ctb] 
Maintainer  Georg Hahn <ghahn@hsph.harvard.edu> 
License  GPL (>= 2) 
Version  1.9 
Package repository  View on CRAN 
Installation 
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