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 ). 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 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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4.1.4 by Giovanny Covarrubias-Pazaran, 3 months 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 rmarkdown, knitr, plyr, parallel, orthopolynom

Linking to Rcpp, RcppArmadillo, RcppProgress

Imported by IPLGP, lmDiallel, mlmm.gwas, pcgen, statgenGWAS.

Suggested by lucid.

Enhanced by emmeans.

See at CRAN