cevcmm: Communication-Efficient Varying Coefficient Mixed-Effects Models

Scalable inference for Varying Coefficient Mixed-Effects Models (VCMMs) with large, correlated random effects. The package implements:

Details

• Sufficient-statistics (SS) iterative estimator (Algorithm 1).

• One-step communication-efficient surrogate likelihood (CSL) estimator.

• SVD-stabilized variants for ill-conditioned random-effect Gram matrices.

• Kronecker and separable covariance structures for origin-destination and group-shared random effects.

The package is in early development. See the project ROADMAP for the current status.

References

Jalili, L. and Lin, L.-H. (2025). Scalable and Communication-Efficient Varying Coefficient Mixed Effect Models: Methodology, Theory, and Applications.

See also

Useful links:

• https://lidajalili.github.io/cevcmm/

• https://github.com/lidajalili/cevcmm

• Report bugs at https://github.com/lidajalili/cevcmm/issues

Author

Maintainer: Lida Jalili lchalangarjalilideh1@gsu.edu

Authors:

• Lida Jalili lchalangarjalilideh1@gsu.edu

• Li-Hsiang Lin lhlin@gsu.edu