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 |

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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