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lfmm  

Latent Factor Mixed Models
View on CRAN: Click here


Download and install lfmm package within the R console
Install from CRAN:
install.packages("lfmm")

Install from Github:
library("remotes")
install_github("cran/lfmm")

Install by package version:
library("remotes")
install_version("lfmm", "1.1")



Attach the package and use:
library("lfmm")
Maintained by
Basile Jumentier
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-06-29
Latest Update: 2021-06-30
Description:
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, ).
How to cite:
Basile Jumentier (2020). lfmm: Latent Factor Mixed Models. R package version 1.1, https://cran.r-project.org/web/packages/lfmm. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0 (2020-06-29 14:24)
Other packages that cited lfmm R package
View lfmm citation profile
Other R packages that lfmm depends, imports, suggests or enhances
Complete documentation for lfmm
Functions, R codes and Examples using the lfmm R package
Some associated functions: effect_size . example.data . glm_test . lfmm . lfmm_lasso . lfmm_ridge . lfmm_sampler . lfmm_test . predict_lfmm . skin.exposure . 
Some associated R codes: Dat.R . LfmmDat.R . RcppExports.R . example_data.R . helpers.R . hypothesis-testing.R . io.R . lassoLFMM.R . lfmm.R . matrix-factorizationR.R . ridgeLFMM.R . sampler.R . skin_exposure.R .  Full lfmm package functions and examples
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