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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1.1 by Basile Jumentier, 5 months ago

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Authors: Basile Jumentier [aut, cre] , Kevin Caye [ctb] , Olivier Fran├žois [ctb]

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