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>).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1 |
rolling linux/jammy R-4.5 | lfmm_1.1.tar.gz |
2.2 MiB |
1.1 |
rolling linux/noble R-4.5 | lfmm_1.1.tar.gz |
2.2 MiB |
1.1 |
rolling source/ R- | lfmm_1.1.tar.gz |
1.5 MiB |
1.1 |
latest linux/jammy R-4.5 | lfmm_1.1.tar.gz |
2.2 MiB |
1.1 |
latest linux/noble R-4.5 | lfmm_1.1.tar.gz |
2.2 MiB |
1.1 |
latest source/ R- | lfmm_1.1.tar.gz |
1.5 MiB |
1.1 |
2026-04-26 source/ R- | lfmm_1.1.tar.gz |
1.5 MiB |
1.1 |
2026-04-23 source/ R- | lfmm_1.1.tar.gz |
1.5 MiB |
1.1 |
2026-04-09 windows/windows R-4.5 | lfmm_1.1.zip |
2.5 MiB |
1.1 |
2025-04-20 source/ R- | lfmm_1.1.tar.gz |
1.5 MiB |