Fast Implementation of (Local) Population Stratification Methods
// Fast implementations to compute the genetic covariance
matrix, the Jaccard similarity matrix, the s-matrix (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 R-package '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, s-matrix, and genomic
relationship matrix without actually computing the similarity
matrices.