This package returns variance-covariance components, BLUPs, BLUEs, residuals, fitted values, variances-covariances for fixed and random effects, etc. The core of the package is the mmer (formula-based) function that fits the multivariate linear mixed models and the predict.mmer function to obtain adjusted means. The sommer package has been developed to provide R users with open-source code to understand how most popular likelihood algorithms in mixed model analysis work, but at the same time allowing to perform their real analysis in diploid and polyploid organisms with small and medium-size data sets ( n problem and dense covariance structures when the direct-inversion algorithm becomes faster than MME-based algorithms. additive, dominance and epistatic relationship structures or other covariance structures, but also functional as a regular mixed model program. Sommer was designed for genomic prediction and genome wide association studies (GWAS) to include i.e. REML estimates can be obtained using the Direct-Inversion Newton-Raphson, Average Information and Efficient Mixed Model Association algorithms coded in C++ using the Armadillo library to optimize dense matrix operations common in genomic selection models. ![]() Sommer is a structural multivariate-univariate linear mixed model solver for multiple random effects allowing the specification and/or estimation of variance covariance structures. ![]() Sommer-package: Solving Mixed Model Equations in R
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