locStra: 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.

Version: 1.7
Imports: Rcpp (≥ 0.12.13), Rdpack, Matrix, RSpectra
LinkingTo: Rcpp, RcppEigen
Published: 2021-01-27
Author: Georg Hahn [aut,cre], Sharon M. Lutz [ctb], Christoph Lange [ctb]
Maintainer: Georg Hahn <ghahn at hsph.harvard.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: locStra results

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Reference manual: locStra.pdf
Package source: locStra_1.7.tar.gz
Windows binaries: r-devel: locStra_1.7.zip, r-devel-UCRT: locStra_1.7.zip, r-release: locStra_1.7.zip, r-oldrel: locStra_1.7.zip
macOS binaries: r-release: locStra_1.7.tgz, r-oldrel: locStra_1.7.tgz
Old sources: locStra archive

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