Title: Fast Implementation of (Local) Population Stratification Methods
Author: Georg Hahn [aut,cre], Sharon M. Lutz [ctb], Christoph Lange [ctb]
Description: Fast and fully sparse 'cpp' implementations to compute the genetic covariance matrix, the genomic relationship matrix, the Jaccard matrix, and the s-matrix of an input matrix. Full support for sparse matrices from the R-package 'Matrix'. Additionally, a 'cpp' implementation of the power method (von Mises iteration) algorithm to compute the largest eigenvector of a matrix is included, and a function to compute sliding windows.
Imports: Rcpp (>= 0.12.13), Rdpack, Matrix

Description: 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. 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, and a function to compute sliding windows. 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.
Imports: Rcpp (>= 0.12.13), Rdpack, Matrix, RSpectra

Description: 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.