Solving Mixed Model Equations in R

Structural multivariate-univariate linear mixed model solver for multiple random effects allowing the specification of variance-covariance structures for random effects and allowing the fit of heterogeneous and special 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) to include multiple relationship matrices or other covariance structures. Spatial models can be fitted using the two-dimensional spline functionality in sommer.


Reference manual

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3.7.3 by Giovanny Covarrubias-Pazaran, 16 hours 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.

See at CRAN