A set of tools for inspecting and understanding R data
structures inspired by str(). Includes ast() for visualizing abstract
syntax trees, ref() for showing shared references, cst() for showing
call stack trees, and obj_size() for computing object sizes.

crayon, methods, prettyunits, rlang (>= 1.0.0)
covr, pillar, pkgdown, testthat (>= 3.0.0)

Fast Implementation of (Local) Population Stratification Methods
Georg Hahn [aut,cre], Sharon M. Lutz [ctb], Christoph Lange [ctb]
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.

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.
Rcpp (>= 0.12.13), Rdpack, Matrix, RSpectra

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.