lfmm: Latent Factor Mixed Models

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: 1.0
Depends: R (≥ 3.2.3)
Imports: foreach, rmarkdown, knitr, MASS, RSpectra, stats, ggplot2, readr, methods, purrr, Rcpp
LinkingTo: RcppEigen, Rcpp
Suggests: testthat
Published: 2020-06-29
Author: Kevin Caye Basile Jumentier Olivier Fran├žois
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


Reference manual: lfmm.pdf
Vignettes: Overview of R Package lfmm
Package source: lfmm_1.0.tar.gz
Windows binaries: r-devel: lfmm_1.0.zip, r-release: lfmm_1.0.zip, r-oldrel: lfmm_1.0.zip
macOS binaries: r-release: lfmm_1.0.tgz, r-oldrel: lfmm_1.0.tgz


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