Fast and accurate inference of gene-environment associations (GEA) in genome-wide studies (Caye et al., 2019, <doi:10.1093/molbev/msz008>). We developed a least-squares estimation approach for confounder and effect sizes estimation that provides a unique framework for several categories of genomic data, not restricted to genotypes. The speed of the new algorithm is several times faster than the existing GEA approaches, then our previous version of the 'LFMM' program present in the 'LEA' package (Frichot and Francois, 2015, <doi:10.1111/2041-210X.12382>).
Version: 1.1 Depends: R (≥ 3.2.3) Imports: foreach, rmarkdown, knitr, MASS, RSpectra, stats, ggplot2, readr, methods, purrr, Rcpp LinkingTo: RcppEigen, Rcpp Suggests: testthat Published: 2021-06-30 DOI: 10.32614/CRAN.package.lfmm Author: Basile Jumentier [aut, cre], Kevin Caye [ctb], Olivier François [ctb] Maintainer: Basile Jumentier <basile.jumentier at gmail.com> BugReports: https://github.com/bcm-uga/lfmm/issues License: GPL-3 NeedsCompilation: yes CRAN checks: lfmm results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=lfmm to link to this page.
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