Latent Factor Mixed Models

Fast and accurate inference of gene-environment associations (GEA) in genome-wide studies (Caye et al., 2019, ). 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, ).


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Reference manual

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install.packages("lfmm")

1.0 by Basile Jumentier, 15 days ago


Report a bug at https://github.com/bcm-uga/lfmm/issues


Browse source code at https://github.com/cran/lfmm


Authors: Kevin Caye <[email protected]> Basile Jumentier <[email protected]> Olivier Fran├žois <[email protected]>


Documentation:   PDF Manual  


GPL-3 license


Imports foreach, rmarkdown, knitr, MASS, RSpectra, stats, ggplot2, readr, methods, purrr, Rcpp

Suggests testthat

Linking to RcppEigen, Rcpp


See at CRAN