Solving Mixed Model Equations in R

Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects and unknown variance-covariance structures (i.e. heterogeneous and unstructured variance models) (Covarrubias-Pazaran, 2016 ; Maier et al., 2015 ). ML/REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms. Designed for genomic prediction and genome wide association studies (GWAS), particularly focused in the p > n problem (more coefficients than observations) and dense known covariance structures for levels of random effects. Spatial models can also be fitted using i.e. the two-dimensional spline functionality available in sommer.

To install this source on your computer type in the R terminal




Reference manual

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4.0.4 by Giovanny Covarrubias-Pazaran, 24 days ago

Browse source code at

Authors: Giovanny Covarrubias-Pazaran

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp

Depends on Matrix, methods, stats, MASS, lattice, crayon

Suggests knitr, plyr, parallel, orthopolynom

Linking to Rcpp, RcppArmadillo

Imported by mlmm.gwas, pcgen.

Enhanced by emmeans.

See at CRAN