Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB

Multivariate random effects with unstructured variance-covariance matrices of large dimensions, q, can be a major challenge to estimate. In this paper, we introduce a new implementation of a reduced-rank approach to fit large dimensional multivariate random effects by writing them as a linear combination of d

• MU
  McMaster University
• AC
  Analytical Center for the Government of the Russian Federation
• UO
  University of New South Wales