Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016; Maier et al., 2015; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.

You can install the development version of `sommer`

from
GitHub:

`::install_github('covaruber/sommer') devtools`

- Quick start for the sommer package
- Moving to newer versions of sommer
- Quantitative genetics using the sommer package
- GxE models in sommer
- lme4 vs sommer

The sommer package is under active development. If you are an expert in mixed models, statistics or programming and you know how to implement of the following:

- the minimum degree ordering algorithm
- the symbolic cholesky factorization
- factor analytic structure
- generalized linear models

please help us to take sommer to the next level. Drop me an email or push some changes through github :)