sommer (2.7)

Solving Mixed Model Equations in R.

Multivariate linear mixed-effect model solver for multiple random effects allowing the specification of variance covariance structures. ML/REML estimates are obtained using the Direct-Inversion Newton-Raphson (NR), Direct-Inversion Average Information (AI), Expectation-Maximization (EM), or Efficient Mixed Model Association (EMMA) algorithms. Designed for genomic prediction and genome wide association studies (GWAS) to include additive, dominance and epistatic relationship structures or other covariance structures in R, but also functional as a regular mixed model program. Multivariate models (multiple responses) can be fitted currently with NR, AI and EMMA algorithms allowing multiple random effects as well. Covariance structures for the residual component are no longer supported.

Maintainer: Giovanny Covarrubias-Pazaran
Author(s): Giovanny Covarrubias-Pazaran

License: GPL-3

Uses: MASS, Matrix, knitr

Released about 1 year ago.