Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. The core computational algorithms are implemented using the 'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'.
Extension of the popular lme4 package from Bates et al. to easily fit the most popular breeding models heterogeneous and unstructured variance models, genomic models, competition models, overlayed models, etc.
You can install the development version of lme4breeding from GitHub:
remotes::install_github('covaruber/lme4breeding')
In addition, the package is also available on CRAN:
install.packages("lme4breeding")
The package is equiped with many datasets and vignettes to learn how to use the package.