Package: lfmm
Type: Package
Title: Latent Factor Mixed Models
Version: 1.1
Date: 2021-06-29
Authors@R: 
  c(person(given = "Basile",
           family = "Jumentier",
           role = c("aut", "cre"),
           email = "basile.jumentier@gmail.com"),
    person(given = "Kevin",
           family = "Caye",
           role = "ctb",
           email = "kevin.caye@gmail.com"),
    person(given = "Olivier",
           family = "François",
           role = "ctb",
           email = "olivier.francois@univ-grenoble-alpes.fr"))
Description: 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>).
License: GPL-3
LazyData: TRUE
Encoding: UTF-8
Depends: R (>= 3.2.3)
Suggests: testthat
Imports: foreach, rmarkdown, knitr, MASS, RSpectra, stats, ggplot2,
        readr, methods, purrr, Rcpp
LinkingTo: RcppEigen, Rcpp
VignetteBuilder: knitr
RoxygenNote: 7.1.1
URL:
BugReports: https://github.com/bcm-uga/lfmm/issues
NeedsCompilation: yes
Packaged: 2021-06-30 11:25:43 UTC; jumentib
Author: Basile Jumentier [aut, cre],
  Kevin Caye [ctb],
  Olivier François [ctb]
Maintainer: Basile Jumentier <basile.jumentier@gmail.com>
Repository: CRAN
Date/Publication: 2021-06-30 13:40:02 UTC
Built: R 4.0.5; x86_64-w64-mingw32; 2022-04-18 05:51:56 UTC; windows
Archs: i386, x64
