Solving Mixed Model Equations in R.

sommer: Solving Mixed Model Equations in R

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.

Installation

You can install the development version of sommer from GitHub:

devtools::install_github('covaruber/sommer')

Vignettes

• 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

Development

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 :)