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: |
1.1 |
| 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:
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