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type: software
license: GPL-2.0-or-later
title: 'locStra: Fast Implementation of (Local) Population Stratification Methods'
version: '1.9'
doi: 10.32614/CRAN.package.locStra
abstract: 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) <https://doi.org/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 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.
authors:
- family-names: Hahn
  given-names: Georg
  email: ghahn@hsph.harvard.edu
repository: https://ghahn-hsph.r-universe.dev
commit: da62338e09b9d1b72c689118c354630ccfbc1509
date-released: '2022-04-07'
contact:
- family-names: Hahn
  given-names: Georg
  email: ghahn@hsph.harvard.edu