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 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.
Imports: Rcpp (>= 0.12.13), Rdpack, Matrix, RSpectra, bigsnpr

Title: Localization Processes for Functional Data Analysis
Authors@R: c(person("Antonio", "Elías", email = "antonioefz91@gmail.com",
person("Raul", "Jiménez", role = c("aut")),
person("Joe", "Yukich", role = c("aut")))
Description: Implementation of a theoretically supported alternative to k-nearest neighbors for functional data
to solve problems of estimating unobserved segments of a partially observed functional data sample,
functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample.
Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria.
The package allows the implementation of the methodology and the replication of the results in Elías, A., Jiménez, R. and Yukich, J. (2020) <arXiv:2007.16059>.